(NM): These kind of applied physics, they can make the difference right now. In the product and in the process the people are using and needing right now.

(NK): HM Hör mal rein. Der Podcast der Studienberatung. Episode 25 – Engineering Physics and Data Science. Hello and welcome to a new episode of HM Hör mal rein. Podcast der Studienberatung. Podcast of the student advisory service. My name is Nikolas Kipp. I am a student advisor at Hochschule München University of applied sciences. And today we do the first episode in English because we are talking about one of our new bachelor programs, that is held completely in English. The name of this program is “Engineering Physics and Data Science” and we will look into what this program has to offer for you. And with me today are professors Moreira, Braun and Schmid. And we will talk about the new undergrad program. Hello to you all. Thank you for your time. And lets start with a short introduction of yourselves please.

(NM): Okay, so I go first. My name is Ney Moreira. I am a professor of physics, with focus on modelling & simulation and I am involved in the coordination of this new international program in engineering physics & data science.

(PS): Hey. My name is Philipp Schmid. I am a professor for Finite Element Methods and their application and I am also part of the organization team for the new undergraduate program.

(GB): Hello, my name is Georg Braun. I am a professor for Computer Science, Microcontrollers and Internet-of-Things.

(HM): So and can you each tell me a little bit about your background? How have you come to be professors.

(NM): So I go first again. So I was born in Brazil. I got my bachelors and my masters degree in Chemistry back there in brasil, at the University of Campinas. And at that time I was heavily focused on computational physical-chemistry, which is more like physics than chemistry, if you ask me. So in 2007 I took a big turn and I came to Germany in the University of Bremen to work on my PhD work in computational physics and materials science.

(NM): And after my PhD work I turned to work ten years, or about ten years, as an industrial researcher at Bosch, where I was mostly involved with modelling materials used in energy conversion devices such as fuel-cells and photovoltaic devices for example. And then after this period I applied here in the university of applied sciences. Since then I am a professor of physics.

(PS): I studied electrical engineering at the University of Ulm, where I also got my PhD. And during my PhD work on micromechanical switches based on diamond films, I used the Finite Element simulations to model the behavior of micromechanical devices. And these simulations then became my main focus.

(PS): After my time at the University of Ulm, I worked for 17 years as a simulation engineer and managing director at an engineering office for finite element simulations. And, yeah, after that time, I came to the university of applied sciences to continue working on these finite element simulations.

(GB): Okay, so, I received my diploma in Electrical Engineering at the Hochschule Technical University in Regensburg, here in Bavaria. With that, I started as a circuit design engineer for ferroelectric memories at the Corporate Research Department with Siemens. During that time, I also got my PhD in Engineering Sciences at the Technical University in Berlin. After that, I worked for Infineon as a system and concept engineer for high-performance memory architectures in computer systems.

(GB): And after about 13 years in the semiconductor industry, I switched to the software side of engineering science at ITK Engineering and for MTU Aero Engines. I found it really exciting to understand both, hardware as well as software, and I am really happy that I can teach both - on both disciplines here at the Hochschule München University of Applied Sciences.

(NK): Yeah, so we have a very varied field of expertise with you three here. Very nice. So what is your function in this new undergraduate program?

(PS): Besides preparing and giving our lectures, we are also involved in the organization of the new Program, which means we also take care of the curriculum and make sure the students will learn the skills they need for their careers.

(NK): And in which fields do you teach exactly?

(NM): Well I have both basic level physics classes and some more advanced courses as well. So currently my main focus is on computer methods applied to solve physics and engineering problems. And we need this kind of stuff because often the problems in physics and engineering, they are way too complicated for using only paper & pencil. So we can not get the solutions we need. So what we need to do on a regular basis is using computer power to get the solutions we want. But before we can get to do that, we need to “teach” the computers how to handle the calculations. And we need to instruct the computer with very specific procedures. So that what I try to teach in my classes are some of these calculation procedures which I think are the most powerful and useful in computational physics.

(PS): I teach an advanced course on multiphysics simulations using the finite element method. This method is one of the most widely used for calculations in engineering and physics and its nowadays an integral part of the product development process. It helps to understand effects, which are difficult or impossible to observe or measure. In this course I give, students learn to simulate physical phenomena like heat conduction, fluid flow, mechanics and electromagnetic fields and how they interact.

(GB): In Engineering Physics and Data Science, I am responsible for Computer Science I and II. There students will learn to use Python as their programming language of choice. We will take it from the ground all the way up to a skill level that allows them to utilize multi-core systems and parallel computing. In addition, we will learn how to utilize important libraries which are fundamental in the world of data science.

(GB): In other curricula, where I am also teaching, I have lectures in microcontrollers, internet-of-things and cyber physical systems. The focus there is more on the hardware and sensor side, as well as data transfer, network communication protocols and data storage. And there is of course the fascinating area of Edge-AI, which brings machine learning towards the edge devices of a computer network, which means closer to the sensor devices.

(NK): Alright, so we already heard a bit of what the new undergrad program has to offer, what it entails. But before we dive deeper into this, let me ask you all: Do you also do research? And if so, what is your field of research, Mr. Moreira?

(NM): Yes, I do so research. So I´m currently involved in different activities. I like this multi-tasking approach. But in all of my activities I use computers to solve applied physics problems.

(NM): For example, we are recently using computational methods to study Silicon Carbite. And Silicone Carbite is important because it can be used to make high voltage electronic switches, which in turn are necessary for controlling electric vehicles, for example, like battery cars. And in this project, I collaborate with a colleague of mine, Prof. Kersch, you probably know him, who is the leader of the project. And we are also collaborating with a leading electronic company that is based here in Munich. This is one project.

(NM): In another project that I have, I have a collaboration with some sports scientists from Brazil, so back home, where we use data-science methods to analyze force signals along physical exercise. Like when people are working out at the gym for example. And this might be important to help trainers to better prepare their athletes or maybe even to help medical doctors to diagnose tremor disorders like Parkinson´s disease. So this is why we are interested in this kind of stuff.

(NK): And what about you Professor Schmid? What is your field of research?

(PS): I’m part of different research activities, where I employ the finite element method to gain theoretical insights into the physical phenomena involved. Examples for that are the ultrashort laser pulse ablation of metals (that is laser cutting), or the nano-indentation of biological structures to determine their properties and also possible damages. And another field is the use of optical fibers for sensing applications. And in all these fields the finite element simulations help to understand the processes in these applications and allow studying properties, which are not accessible otherwise.

(NK): And Professor Braun, do you also do research besides teaching?

(GB): Not really research – but besides my lectures, I am leading a team of software engineers working on EXaHM. EXaHM is a unique and very innovative framework for competence- and application-oriented, digital examinations. It allows us to use regular Windows PCs with typical engineering application software in an isolated and protected environment. EXaHM is utilized at the Hochschule München as well as other Universities for digital exams.

(NK): Yeah, that’s very interesting, very futuristic fields. Not really an engineer myself I have to admit. But sound very very interesting, all of it. But now to the undergrad program. What exactly is engineering Physics & Data Science?

(NM): Okay, so, I have to go by parts here. In general, Engineering is kind of easy to define: So it is the application of physics and science to real-life problems, products and processes, if you want. It is the kind of science you see in daily life, like the microchips on your mobile phones or in the instruments used by your medical doctor, this kind of stuff. And engineering physics is a specialization of engineering, which is focused on applied physics and especially in high-technology fields such as micro- & nanotechnology, photonics and semi-conductor physics to give you here some examples.

Data-science on the other hand, is something more recent and you will find different definitions for it. The definition I like most is the one from IBM, the computer company, who says that data science is the combination of three different foundations:

Data-science on the other hand, is something more recent and you will find different definitions for it. The definition I like most is the one from IBM, the computer company, who says that data science is the combination of three different foundations: So, the first one is Mathmatics and Statistics. The second one is Computer Science & Artificial Intelligence, or AI. And the third one, which nobody talks about, is specialized domain expertise. And I repeat specialized domain expertise.

Data-science on the other hand, is something more recent and you will find different definitions for it. The definition I like most is the one from IBM, the computer company, who says that data science is the combination of three different foundations: According to this definition from IBM, there is no such a thing like “the data-science”. But there are many different science and engineering fields that can benefit from a more modelling- and data-oriented approach.

Data-science on the other hand, is something more recent and you will find different definitions for it. The definition I like most is the one from IBM, the computer company, who says that data science is the combination of three different foundations: And it turns to be that in our case, the domain expertise is the field of engineering physics. So our objective in our program is to prepare the students for applying physics to real-life problems, products and processes by using a modern modelling- and data-driven approach. And we want to do that, because people with this kind of expertise are very very scarce and highly demanded in today’s industry. So that’s what we want to do.

(NK): Why are those skills so demanded today? Whats the need?

(NM): The reason for that is that the products of today they are designed to be “smart”. And what does that mean? That means that the products cannot only do something useful for you, but they can also react to changes in order to do it better.

And I´ll give you a simple example here: for example the display of my phone. It automatically shines brighter when I walk under the sun. So my phone has a sensor built in, it “feels” how bright the day is actually. It also has a piece of software that can not only interpret the data from the sensor, but it also tells the rest of the phone what to do – like for example regulating the brightness of the screen. There are also different examples that you can use in the automobile industry, but I guess you got the main idea here. The bottom line is that the modern products, which we see in the market today, they are packed with sensors and there is an increasing amount of data available for those who can do something useful with this data. And this is a great perspective for our students.

(NK): So there are also already other degree programs for data science, also called data science. There is an undergrad program called data science at our university. So what is so special about Engineering Physics and Data Science?

(GB): Well most of the other data science programs that I know, they start at computer science with machine learning and deep learning supported by mathmatics and statistics. This is usually a quite broad field and towards the end of these programs, it’s usually up to the students to find their individual field of application and domain knowledge.

(GB): In Engineering Physics and Data Science, we start at the domain knowledge, which is engineering physics and with the help of mathmatics, statistics and computer science, we expand into the field of data science.

(GB): I see a big advantage for our students, because they know, where their data is coming from, either from simulation or from sensors – or even from both. They know the nature and the meaning behind the data. And they immediately see when some data doesn’t make sense. And with such elaborate domain knowledge, they will become brilliant data scientists.

(NK): Alright. So, it turns around the focus I guess a bit of data science, like you said before. But isn’t this too much focus upfront? Is it too narrow? Is it too specialized?

(GB): Yeah, I hear you. To be honest, I don’t think so. The domain knowledge that we are talking about here is Engineering Physics. So it’s still quite broad compared to other disciplines.

(NK): Okay, so the “problem” for such programs is that they touch on very different fields. And they cant sometimes go in deep enough. So my question for this program is: How much physics is in there and how much Data-Science?

(NM): Well that is a very good question actually.

(NM): Because of the focus in the modelling and the data-driven methods, it is clear that we have to compromise somewhere. Thus, there is a little less space for pure physics in comparison with traditional physics programs.

(NM): However, our program is all about applied physics and therefore we can fit more computer & data-science into the program. For example, we do not offer courses in elementary particles or cosmology, which are very interesting and important topics, if you ask me, but are generally not applicable to engineering problems. So what we offer to our students instead are courses in computer sciences and electrical engineering for example, where they can learn how to write a code. Where they can learn how to build a sensor system to acquire data if they like to. These are the kind of skills our students will use on a daily basis along their professional careers.

(NM): So in other words, our objective here is providing a well-balanced mix of physics, engineering and computer sciences all in a single curriculum, so that the students can learn the tools they need in order to work on data-driven engineering problems

(NK): Alright, that seems fair and can you give some more detail about the structure of the program? How do the students start, how do they commence…

(NM): The program is basically split in three different phases: So, one phase is introductory courses, and then there is advanced courses and then it comes to specialization.

So lets go by parts. In the introductory courses, there will be about 30% of physics, which includes the basic stuff: mechanics, thermodynamics, electromagnetism, and so on. There is about 30% of introductory mathematics, including calculus, linear algebra and statistics. And 20% of computer science and programming and an additional 20% of introduction to engineering – including materials technology & electrical engineering. This is the basic stuff. Its basically the program of engineering we find everywhere.

(GB) Maybe, I can jump in here and provide some details for computer science in the first two terms: We will start with very basic programming skills in Python. Besides “normal” programs and integrated development environments, we will also work with Jupyter Notebooks which seem to become a de-facto-standard in the world of science and engineering.

(GB) Maybe, I can jump in here and provide some details for computer science in the first two terms: After that, we’ll be switching gears and enter the ecosystems of Python libraries and modules, such as NumPy, SciPy and Pandas. This will build the foundation for later courses like Machine Learning, Algorithms and Data Structures.

(GB) Maybe, I can jump in here and provide some details for computer science in the first two terms: (NM) Right. And these courses Georg is talking about belong to the second stage – to the advanced courses where the data-science aspect really starts to kick in. So after the basic courses with the basics on point, now the students can put everything they learned in physics, in math, in computer science and get them to work together. And this is really where the thing starts to happen here. In this second phase, about one third of the program is dedicated to topics such as Machine Learning and numerical methods, which are useful tools for solving physical problems on the computer.

(GB) Maybe, I can jump in here and provide some details for computer science in the first two terms: The engineering part of this phase is also more modelling- & data-driven oriented. So there are courses in Simulation & Control, Sensor technology, Signal processing - which are essential tools to modern engineering. And this part also makes about one third of the cycle.

(GB) Maybe, I can jump in here and provide some details for computer science in the first two terms: About 20% of this second cycle, of these advanced courses, is still dedicated to physics, but the focus here is on more advanced stuff and more technology oriented stuff. Like for example, we have courses on acoustics and optics, which obviously applied physics. Just think about how many microphones and cameras there are in a single smart phone for example.

(GB) Maybe, I can jump in here and provide some details for computer science in the first two terms: And to finalize the second phase the students will also learn more advanced mathematics, like differential equations for example, which is the language we use to express basically all existing physical laws and advanced engineering problems.

(NK): Alright, so lots of lots of input. Very different fields. Seemed all a bit theoretical to me. But we as a university of applied sciences we always look for the point where our students can apply their skills and when is that point in the new program?

(PS): There are several opportunities along the program, like in practical lab or computer classes. But the first big opportunity is already in the 5th semester, when the students will have their industrial internship. It is very important that the students gather some real working experience early in their careers.

In this internship semester, the students can apply their skills to real-world challenges and experience the kind of working environment they will likely find later in their lives. Most students do their internships in one of the many technology companies around Munich. But there are also opportunities in several research institutes or university labs. The bottom line is: here is where the students start to go after their own personal objectives and to shape their professional paths.

(NK): So they start their specialization in this phase, yeah?

(PS): Yeah, this is the beginning of the specialization cycle. The industrial internship make the start of this phase, which then extends along the two final semesters of the program. During the 6th and 7th semesters, the students take elective courses, which will allow them to customize their curriculum. These elective courses amount to about 50% of the final two semesters and the students can choose among several different topics such as semi-conductors physics, micro & nanotechnology, photonics or energy conversion.

(NK): And the remaining 50% of the final credits? Where do they come from?

(PS): So the 6th semester still contains two mandatory courses

(PS): The first one is solid state-physics, which is based on modern quantum technology and is the basis for modern electronic technology. And the second is Multiphysics Simulation, where the students will use advanced simulation tools to investigate complex systems. And by complex, I mean systems where different physical phenomena are constantly influencing each other. This kind of systems are very common in real life applications and simulation engineers often use computer models to understand how they work.

(PS): And second, the students are expected to develop their Bachelor thesis in the 7th semester – which will take about 40% of their time. Here again the students can develop their professional lives either in the industry or in the research ecosystem around Munich.

(NK): Alright, so, we heard a lot about whats in the program and how its build up and it contains a lot of physics. Physics is in the name. I guess out there there are a lot of misconceptions - typical misconceptions – when it comes to physics. What are those typical "misconceptions?

(NM): There is one frequent overarching question about the boundaries of the program.

(NM): Because some people ask if they will be learning “real physics” in the Program. I think that this question it comes from how physics is usually portrayed on popular media. So if you take a look on how people get to know physics on social media for example, it is usually about something around cosmology, particle physics or some philosophical discussion about quantum mechanics; which I agree are very fascinating and important stuff. In fact, my PhD was based in quantum theory, if I’m being honest. I love quantum theory. However, what appears in the media is just a small fraction of what physics is and it is not a very representative one if you ask me.

I’ll give you an example for this: According to a poll from the American Institute of Physics couple of years ago, about 40% of physicists end up working on the private sector. And that is excellent. Because they work as engineers, they work as computer scientists or even as finance experts in some cases. These 40% of applied physicists tend to be some of the best payed professionals in today´s industry and economy. So, nobody talks about it but there are a lot of physicists doing interesting stuff and receiving good money for this.

But now coming back to the original question: Do our students learn “real physics” here. Absolutely! They definitely do. But focusing on applied sciences, which is exactly what they need to excel in a technology oriented career. So that’s the kind of stuff they are going to need in the industry. However, I need to make clear at this point, that the problems in applied physics, they are by no means less interesting or challenging than the problems in academic physics. They are just more focused on applications. If you ask me, this kind of applied physics problems they are even more exciting than traditional academic physics, because these kind of applied physics, they can make the difference right now, in the product and in the process that people are using and needing right now. So and that’s a big difference for me.

(NK): Yeah. Talking about making a difference, topics that are currently very important: How much artificial intelligence is in the new program?

(NM): This is similar to the question about “real physics”, because people also ask about “real artificial intelligence” being part of the program or not.

(NM): This question comes again from how AI is portrayed in the general media. You have seen in the last couple of years AI has been really hyped, especially after the release of ChatGPT. And because of that, for most people, something like ChatGPT is what comes to mind when they think about AI. But again, this is not the whole truth. In fact, AI is kind of an “umbrella term”, which encloses several different technologies. Large language models such as ChatGPT are one kind of this AI term. But there are also other technologies.

(NM): For example, there is this company called DeepMind, which is an arm of Google, who has predicted the properties of 2.2 Million new, so far non-existing materials by using their neural-network program. This is very impressive. So they study materials that have never been synthesized and they know the properties of these materials, just by analyzing the data they have at hand. This kind of AI is much more relevant to us as engineers than large language models such as ChatGPT, for example.

(NM): Back to the question, yes, our students will definitely learn AI techniques here. There are disciplines dedicated to machine-learning and neural networks in the curriculum – which are specific kinds of AI used in science and technology. These tools can eventually direct one towards to more general AI technologies – such as those required for self-driving cars, for example. So yeah, the short answer is yes, definitely.

(NK): And are there, like, skills someone should have if they would be interested in studying Engineering Physics and Data Science?

(NM): I think that there are three important characteristics you should have if you want t engage in this kind of study. First is curiosity. If you like to understand how things work, you will find very exciting topics to learn and probably will have a very good time here.

(NM): So the second is motivation. Because as professors what we can do is offer a thoughtful program and well prepared learning materials, and we definitely do that. But this alone is no guarantee for a successful learning process. The student must invest the necessary effort as well. The more effort you put in your studies, the more success you will experience along the program. That’s for sure. So motivation is very important.

And the third and maybe one of the most important ones is resilience. Because, no matter what subject you choose to study, any serious program will be challenging from time to time. For getting a degree, you must learn several new things; and this is no way easy. But it should not keep you from progressing towards your goals. In fact, being challenged is a natural part of learning. There is no way around it. But the good news here is: The more you learn, the easier it becomes. And with time you will start to connect the dots among different subjects like physics and computers and mathematics. You will start to put everything together and you will develop your own understanding about these topics. You just have to embrace the process and I guarantee you that good things are gonna happen.

(PS): Of course, it helps when those interested have had some contact with physics and computation and hopefully they have enjoyed that contact as well. I mean, they might have worked on something scientific or technical; they have programmed a computer or read books on physics, computer science and technology. If you like that sort of thing, you've come to the right place; because if you end up working in engineering physics and data science, you will spend most of the time doing this.

(NK): Yeah, so the things you said, Professor Moreira, I guess are characteristics everyone should have when starting in a study program but are very very good points. For me as a student advisor it is necessary to mention at this point, you would also need to be able to speak English. You need to have at least a level of B2 in English to be able to start this program. And for international students it needs to be a german speaking level of at least A2. And the german speaking level you would have to prove this by the end of the second semester. You don’t need it right at the start. Just to mention this at this point. So, after the study program, what are possible career paths? Where can one work when having finished this program?

(NM): That’s a good question. What we are trying to do here is this mix between engineering physics and data science and this is very innovative. And I am sure that it will open up several different career paths for the students. And why do I know that? Because I`ve seen that happen, actually. In the ten years, or around ten years I´ve worked in the industry, I saw the profile of the open positions changing along the time. I remember when I started over there, skills in computational physics were maybe nice to have, but they were not a must-have at that time. However after these ten years, before I left to come here to the Hochschule München University of applied sciences, my previous employer was offering courses on computer sciences to old physicists, who used to work there. That´s because they could not fill the positions requiring both physicists and data scientists. And I am sure that this demand for physical engineers with a strong data-science foundation is already strong and it will increase along the next decades. I am sure.

(NM): But with that being said, here are some possible career paths for a student from our new program.

(NM): So the student could start in the industry right after the Bachelor degree for example because the demand is already there. So if that’s what they want, they can do that. For example just yesterday I searched for Physics and Data-Science in a regional job-portal and I got over 6000 hits. So that’s a lot of jobs over there. Another possibility is, if the students want to chase further education, they can do that in a masters program and later on even in a PhD work. Within this path, the students have the choice between the industry as they have with the bachelor only or if they would like to, they can also pursue a more research oriented career if that’s what they want. Both things are possible, both things are very attractive. And I can tell about my own experience because we can flip even then. So I’ve worked first in my PhD work, and then I worked in the industry, now I am into education again. And I am very happy with this choice and I would recommend that for any student to at least give a try.

(NK): Yeah, I would also say, this is a very versatile program, you could do lots of things with it. And its very, you know, focused on the future. And I guess you could make worse choices, when choosing a study program.

(NM): For sure.

(NK): So, thank you very much for your insight in this new program. Very excited for its start in the next winter semester. Do you have anything missing now, anything you would want to say to people who are interested in studying at Hochschule München?

(NM): So, Mr. Kipp, first of all thank you very much for your time and I hope that everybody has enjoyed the podcast. I’ve enjoyed that quite a lot. For the people who are listening and the people who are thinking about maybe to start to study. If you are thinking about it, I would like to tell you:

(NM): If you like science, technology and computers; If you want to make your mark in the world right now; Not in the future, right now. I want to invite you again to our program in Engineering Physics and Data science. We are 100% committed to teach you the skills you will need to succeed in your career. If you have any further question about the program, please contact me via my e-mail at ney.moreira@hm.edu. So I hope that you can find the link in the description of the podcast.

(NK): Of course I will put it there, yeah.

(NM): Thank you very much. So if you have some questions, just drop me a line and I will try to answer that as soon as possible. And maybe as a final thought, because our public is probably thinking about to move to Germany: What I would like to tell to you is that pursuing higher education is a journey which can not only put you in a better professional place in life, but can also make you better in general. Because education helps you to understand things better. This is how it works. And this is generally true for everybody, but it is especially true if you are living in a new culture at the same time. So if you are moving to Germany and experience this new culture, its going to be an awesome experience. So for our listeners from outside Germany, what I would like to tell you is: Don’t be afraid to apply. Germany is a very welcoming place and I know that because I have built my life here. So I came as a foreigner and I found awesome caring people here in Germany, and I can say that because some of them are here in this room with me, like my colleagues. And I could develop into the best version of myself here. And we all want you to develop into the best version of yourself. And we are here to help you. So if you want to apply, I welcome you here, so I hope to see you soon in Munich.

(NK): Yeah, those are very great words for the end. Thank you all again very much for your time. And everybody listening, have a great day. And you can start applying to this new program beginning of may and until the fifteenth of July. So see you soon, at the Hochschule München.

(NK): HM Hör mal rein – der Podcast der Studienberatung.