“Many international researchers don’t even know what’s possible in Germany”
How can the gap be bridged between top-level international research and the bureaucracy of German academia? Having worked at prestigious institutions in Boston and Seattle, the biochemist Thomas Schlichthärle has decided to return to Germany – despite lucrative alternatives in the USA. In this interview, he talks about $250,000-plus salaries, untapped start-up potential, and what would make the German science system more dynamic.
Why did you decide to return to Germany?
At first, I wondered whether I should move to a start-up or what’s known as a “focused research organization” (FRO). The USA is very attractive in that sector. Those organizations pay salaries universities can’t match – $250,000 a year is common. US universities are attractive too, particularly in medicine. But the problem there is that a great deal depends on third-party funding, and after three years you’re supposed to be financing your salary yourself. I found the offer from the Technical University of Munich (TUM) – supported with funds from Wübben Stiftung – more convincing. TUM is a “destination university”, it provides a superb research environment, embedded in the strong Munich ecosystem. The city has two first-rate universities, the Helmholtz and Max Planck institutes, and a dynamic start-up culture which is being promoted with targeted initiatives from the TUM Venture Labs.
What do you appreciate about the German system compared to the U.S.?
Fundamentally, it is solidly financed. I don’t want to have to spend time scraping together third-party money to pay my salary every year. What’s more, there are internal funding opportunities and a large number of external funding lines like the ones administered by the German Research Foundation (DFG), the European Research Council (ERC), and various other foundations. Another advantage is the infrastructure. In the USA, you often incur costs for every piece of equipment – $40 an hour to use a microscope for example. All kinds of things are done more informally in Germany: you just ask the laboratory next door. That works well in Munich because the structures are open.
What still needs to be improved from your point of view?
There’s a need for more interdisciplinary structures. The most important scientific equipment – the core facilities – require permanent personnel, “staff scientists” as they are known. There are hardly any of these at German universities. A lot ends up being done by PhD students, which is inefficient. The Max Planck institutes have got the right idea in this respect. We should also be looking more to share the use of high-end infrastructure like GPU clusters for data processing, cryo-electron microscopes, and mass spectrometers – not every lab has to own everything itself. In addition, more tenure track posts would be important, and career paths should make it easier to switch between industry and academia. Extended periods of employment in industry should not be a disadvantage.
Are German universities doing enough to attract international talent?
The offers are competitive—no doubt—but poorly communicated. In the U.S., group leaders are offered a complete package worth $1.5 to $2 million. In Germany, the funding is broken down into separate budget lines, so the big picture isn’t clear. But the overall value is similar. Max Planck has nailed this: they offer a flat €2.7 million, which is very attractive internationally. The problem is marketing. Many international researchers simply don’t know what’s available in Germany. Universities already have world-class equipment that can be shared via collaborations, but that message doesn’t get out. Job postings also aren’t as visible internationally as they should be.
Where do international researchers usually look for jobs in Germany?
That’s exactly the issue—most don’t know where to start. I get the ZEIT newsletter, but that’s very Germany-focused. In the U.S., social media plays a much bigger role. Germany needs a more international recruitment strategy and a more targeted appointment process: identify gaps and actively seek out people to fill them.
How was your own hiring process at TUM?
I applied in June, interviewed in October 2023—then silence. The president reached out personally in May 2024, which made an impression. Negotiations followed in July, and I accepted the offer in February 2025.
Does it always take that long?
Quite often, yes. In my case, the whole process took almost two years. Months passed just between my interview and getting feedback. In Sweden, where I also applied, things moved much faster: application in September, pre-interviews in October, full interview in January, and three weeks later I was on the shortlist—with clear communication throughout.
How much of a factor is language?
A huge one. Most administrative processes are in German. For international colleagues who don’t speak the language, this creates serious hurdles. Even hiring a team assistant becomes difficult when English skills are limited. If Germany is serious about internationalization, universities need bilingual administration across the board.
What else would help new researchers settle in?
A guesthouse, like the one Max Planck offers. Having three to six months of housing takes a lot of pressure off at the start. At TUM, I had great personal support from the School of Natural Sciences, but ideally, research groups should have dedicated, experienced administrative support from day one.
What were your first challenges in Munich?
Managing my calendar (laughs). It sounds trivial, but it requires organization. Otherwise, things went smoothly—I found an apartment quickly. TUM, the Free State of Bavaria, and the Wübben Foundation provided excellent support: two months of rental subsidy in Munich and six months of double rent covered when I was transitioning from Seattle.
Your professorship is in AI-guided Protein Design. What future applications could your research lead to?
There’s currently a global race in drug development to design antibodies entirely by computer, with high experimental success rates. But there’s also a second, equally exciting area: industrial biotechnology. Enzymes play a crucial role—whether in detergents, plastic recycling through PET degradation, or food production. The big advantage is that, unlike drug development, these applications don’t require lengthy clinical trials, so products reach the market much faster. AI methods are now so advanced that we can design enzymes for highly specific tasks.
How competitive is Germany in this field?
Munich now has three protein design groups: Alena Khmelinskaia (LMU), Lukas Milles (MPI/LMU), and myself—all from Seattle, all from the same community. Leipzig has Jens Meiler; Bayreuth has Birte Höcker. The ecosystem is growing, but it’s still young. Europe, however, is waking up: Boehringer Ingelheim is funding a new institute for AI in biomedicine in Vienna, and the Novo Nordisk Foundation just launched a $100 million protein design center in Copenhagen. But when it comes to technology transfer, Germany lags far behind. In the U.S., three major start-ups have already emerged: Xaira Therapeutics raised $1 billion, Generate Biomedicine about $600 million, and Isomorphic Labs—part of Google’s Alphabet—has signed billion-dollar deals with pharma companies. Unless Germany catches up, it risks being left behind.
Why is tech transfer still so weak in Germany?
It’s not about money—it’s about mindset. In the U.S., founders are much bolder, even without social safety nets. In Germany, social security means no one risks ending up on the street. But maybe that very safety slows people down. We need more experimental formats to support small, dynamic teams at the start. The Wübben Foundation’s sandpits are a great model: small, flexible grants to open up new research areas and launch pilot projects. Later, more funding is needed—but as a first step, they’re incredibly valuable.
How did you become focused on AI and computational methods?
A defining moment was my master’s thesis at the Wyss Institute in Boston, where I met Ralf Jungmann. I followed him to Germany and did my PhD at the Max Planck Institute of Biochemistry in Munich, working on super-resolution microscopy and computational data analysis. But we always hit one wall: proteins had to be labeled for microscopy, usually with antibodies. When I heard David Baker speak at the institute, I realized his technologies could design smaller, better-suited labels much faster. That was the spark that led me to join his lab in Seattle as a postdoc.
Did working with a future Nobel laureate shape your career?
Absolutely. When David won the Nobel Prize, it was a special moment. On announcement day, the lab turned it into “Champagne Day” (laughs). It was an exciting time scientifically, too—AI transformed the field fundamentally during the years I was there.
Thank you for the conversation.
Thomas Schlichthärle studied Molecular Medicine in Tübingen and Molecular Bioengineering at TU Dresden. After a research stay at the Wyss Institute in Boston, he began his PhD at the Max Planck Institute of Biochemistry in Munich. He later joined David Baker’s lab at the University of Washington in Seattle, focusing on synthetic proteins for cell growth. In 2025, supported by the Wübben Foundation, he was appointed Tenure Track Assistant Professor (W2) for AI-guided Protein Design at the Technical University of Munich. His research combines machine learning, structural biology, synthetic biology, and biomedicine to develop new protein design methods, with the goal of engineering synthetic proteins that can modulate, detect, or reprogram cellular signaling pathways.