We are always looking for talented and motivated PhD students and postdoctoral researchers who are passionate about (automated) machine learning & meta-learning, recommender systems, information retrieval or some of our other research interests.
- 20 x 2-year PostDoc ‘STAR’ fellowships at the University of Siegen (EU Marie Curie)
- 1 Ph.D. or PostDoc Position in Automated Algorithm-Selection for Recommender Systems (4-5 years; €40k – €77k pa)
- 3 Well-Funded PhD & Postdoc Positions, up to 5 Years at the University of Siegen (Germany): AutoML, Algorithm Selection, Recommender-Systems …
Even if we do not have any open positions (see above), we would still be happy to hear from you. Especially, if you have an exciting research idea and are interested in applying for funding yourself. Potential sources for funding include
- The German Academic Exchange Service DAAD, if you are from abroad
- The German Science Foundation DFG
If you are German and interested to work at Trinity College Dublin or the National Institute of Informatics in Tokyo, the DAAD IFI program may be interesting for you. Let us know if you are interested, we would be glad to support you in applying and establishing contact.
Please note that we cannot help you with finding a suitable topic for your postdoctoral project. As a (potential) postdoc we expect you to have your own research vision.
Even if we do not have open PhD positions (see above), we would be glad to hear from if you are interested in applying e.f. for a scholarship.
— More details coming soon —
Part-Time / Industry
The University of Siegen is perfectly suited for a part-time PhD, i.e. doing the PhD research besides your normal (full-time) job. As one of the very few universities in Germany, the University of Siegen offers a cumulative dissertation. This means, instead of writing a few-hundred-pages-long monograph, you need to publish 4 major research articles as first author (plus an introduction and summary). Those 4 articles should loosely relate to each other, but not as much as if you were writing a monograph.
A cumulative dissertation gives you much more flexibility because you do not have to work on one specific topic over multiple years. Instead, you can have some ‘shift’ in your topic(s). Also, writing up a monograph usually takes months of boring and frustrating work towards the end of your PhD time. When doing a PhD you should publish research articles anyway. Hence, there is almost no overhead with a cumulative dissertation, except writing an introduction and a summary, which should not take more than a few days.
As a side note, there is no tuition fee at the University of Siegen, and you do not need to attend any classes. Only if you want to be officially enrolled as a PhD student, there is a fee of 250 Euros or so per semester but this is voluntary. In addition, you can do the entire Ph.D. remotely. However, it would be nice if in the beginning, the middle, and towards the end of your PhD, you would spend a few days in person here in Siegen.
In summary, all you need for a PhD, is to publish four major research articles. Whether this takes 3 years, 5 years, or 10 years doesn’t matter.
If you are interested in a part-time Ph.D., we would love to hear from you. Please note that if you want to do a part-time Ph.D., then:
- You need to work at a company that supports publishing. This must be evidenced by existing publications of employees of that company.
- You must have already published at least one paper. This could be a workshop paper or whatever, but there must be some evidence that you are capable of writing research articles. Ideally, you have published an article as part of your current work. Alternatively, you might have published e.g. parts of your Master’s thesis.
- Your employer should actively support your Ph.D. This means, your employer should
- Allow you to work at least one day per week on your Ph.D.
- Support you financially with at least 2,000€ per year e.g. to visit conferences.
- Provide you with a computer/laptop to work on
Visiting PhD Students
Contact us, if you would like to work with us for a few months in Siegen. We are always open to self-funded visitors and the DAAD has several programs that may support you.
General Advice on Doing a PhD
A good PhD Topic
A good Ph.D. topic is key to the success of your work. This means, you must have a) identified a specific problem, and b) a plausible idea of how you could solve this problem. Something like “I want to use machine learning to improve abc” is not a suitable Ph.D. topic. Just throwing dozens of machine learning algorithms to a problem and see what algorithm solves the problem best, is not a sufficient Ph.D. contribution. It is important, that your proposed solution is novel, and convincing (ideally other persons familiar with the problem would say “Oh yes, of course, why haven’t I thought of that?).
Doing a PhD is not about obtaining broad knowledge in a field such as machine learning or natural language processing. A PhD is about deepening your existing knowledge and, most importantly, creating new knowledge. “New knowledge” in terms of creating a novel algorithm, designing a new method or metric, …. This means, to start a PhD, you should have already good knowledge in the broader field (e.g. machine learning) and ideally some knowledge in the special field of your PhD (e.g. recurrent neural networks). If that is not the case, you might be better off to do first some online courses e.g. in machine learning; or do a Bachelor / Master (by research) in that field.
Positions at the NII in Tokyo, Japan
We work closely with the National Institute of Informatics (NII) in Tokyo, Japan. If you are interested in working at the NII, have a look at the programmes of the Japan Society for the Promotion of Science. They offer scholarships for foreigners at all career levels, including senior researchers, to conduct research visits in Japan for up to two years. Permanent positions at the NII are advertised on NII’s website. If you are from Germany, the DAAD may have additional programs to fund research visit. As a PhD student, the DAAD may fund you for up to six months, as a postdoctoral researcher up to 2 years.
If you would like to work with us, we would love to hear from you. Please send us a message with the following information.
Everyone (Intern, Visitor, Ph.D., PostDoc…)
- Your current employer/university:
- Current place of residence:
- a) Earliest b) Prefered c) Latest possible start date:
- Min/Max/Pref. Duration of your stay [if applicable]:
- How do you wish to fund your stay [if applicable]?
- Your experience in Machine Learning:
- Your experience in Recommender Systems and Information Retrieval:
- Your experience in AutoML, algorithm selection, optimization, meta-learning, and NAS:
- Your Work/Industry Experience:
- Any notable contributions to software projects e.g. on GitHub?
- Any noteworthy awards?
- Your Motivation / Why do you want to join our group?
- Anything else you would like to tell us about yourself?
- Your questions for us:
(Prospective) Visiting Bachelor/Master Students or Interns Only
- Do you have any publications already (e.g. have you published your Bachelor thesis)?
- In what percentage do you rank among your fellow students (e.g. Top 1%; Top 5%; Top 20%; …):
- How good are your math skills?
- How good are your programming skills?
(Prospective) Ph.D. Students Only
- Type of Ph.D. (Full-Time; Part-Time; Visiting)
- Have you already published? If yes, please provide details:
- Your Google Scholar profile if existent:
- What’s the final grade of your Master’s degree?
- In what percentage do you rank among your graduation class (e.g. Top 1%; Top 5%; Top 20%; …):
- How good are your math skills?
- How good are your programming skills?
- If you are interested in a part-time Ph.D., please briefly explain how your employer will support you:
- If you have a specific idea for a project please let us know [optional]:
(Prospective) PostDocs Only
- Your Google Scholar profile
- Your Top-3 publications in the past 3 years (URLs to full text, please do not attach the publications)
- Your two most successful student supervisions (Bachelor/Master or Ph.D. students)
- Acquired Funding:
- Teaching Experience:
- Your research vision or project proposal: