Application Guidelines

The strongest applications are those that show a clear and specific overlap with our research. Before applying, please do the following:

1. Review our recent research topics and publications.
2. Identify one or two themes that genuinely match your interests and background.
3. Explain in your application why you are a good fit for this lab in particular.

A generic application to “anything in AI” is usually not competitive. A focused application that clearly connects your background to our research is much more convincing. Show why this lab fits you and why you fit this lab. We value applicants who can demonstrate:

• strong analytical and mathematical thinking
• solid programming ability
• curiosity and research drive
• evidence of real work (projects, code, thesis, papers)
• independence and reliability
• clear written and spoken English
• willingness to work carefully and reproducibly

Expectations for candidates vary depending on their level of experience

Student Assistants

Student assistants typically support ongoing research, software development, experiments, data processing, or benchmarking. A strong application usually includes:

• current enrollment in computer science, AI, robotics, electrical engineering, mathematics, physics, or a related field
• solid programming skills, especially in Python
• willingness to work in a structured and reliable way
• interest in machine learning, sensing, localization, embedded systems, or reinforcement learning

Helpful but not strictly required:

• experience with PyTorch or similar frameworks
• familiarity with Linux, Git, Docker, or scientific Python tooling
• experience with signal processing, sensor data, robotics, or wireless systems

We especially appreciate applicants who can show code, project work, or prior research-oriented work.

Bachelor’s / Master’s Thesis Students

We supervise thesis projects for students whose interests strongly align with the lab.

Requirements:

• excellent or very good academic performance in relevant coursework
• prior exposure to machine learning, statistics, optimization, signal processing, robotics, or related areas
• the ability to work independently and systematically
• interest in reading scientific literature and implementing reproducible experiments

For thesis applications, it is very helpful if you include:

• a short indication of which topic area interests you most
• relevant coursework and grades
• one or two relevant projects
• your availability and intended thesis timeline

A thesis in our lab is expected to go beyond implementation only. We value careful experimentation, clear problem formulation, and scientific writing.

PhD Candidates

We are looking for PhD candidates who want to conduct ambitious research in machine learning with a focus on positioning systems (but not exclusively!).

General requirements:

• an excellent Master’s degree in computer science, AI, mathematics, electrical engineering, robotics, physics, or related disciplines
• strong foundations in at least some of the following: machine learning / deep learning / reinforcement learning, probability and statistics, strong implementation skills, evidence that they can work independently on difficult technical problems

What we look for in your application:

• an outstanding Master’s thesis
• publications, preprints, or strong research projects
• open-source contributions or substantial code artifacts
• internships in relevant research environments
• clear motivation for research rather than only engineering

PhD candidates should be excited about pushing beyond established solutions, formulating precise research questions, and producing work that can stand up to serious scientific scrutiny.

Postdoctoral Researchers

We welcome applications from postdoctoral researchers who want to help build a strong and internationally visible research program in the lab.

Requirements:

• a PhD in a relevant field
• a strong publication record in respected venues
• the ability to initiate and drive research projects independently
• experience mentoring students or junior researchers
• excellent communication skills and scientific judgment

Particularly valuable are applicants who can contribute to one or more of the following:

• strengthening the lab’s visibility through publications, mentoring, and academic community engagement

• advancing a core methodological area of the lab

• building bridges to adjacent fields or application domains

• helping shape larger collaborative or externally funded research efforts

Please send your application as one single PDF.

Your application should include the following:

1. Motivation Letter or Statement of Interest (maximum 1 page)

Please address:

• why you want to join the Machine Learning and Positioning Systems lab
• which of our research directions are most relevant to you
• what you have done so far that prepares you for this position
• what kind of research or project questions you would like to work on
• In case of PhD candidates: please explain why you want to conduct a PhD thesis. What do you expect?

This should be specific and concrete. Please avoid generic statements.

2. Curriculum Vitae

Please include:

• education
• research experience
• work experience, if applicable
• technical skills
• programming languages and tools
• publications, if applicable
• awards, scholarships, or distinctions, if applicable

3. Transcripts and Certificates

Please include the most relevant academic records, for example:

• Bachelor’s transcript
• Master’s transcript
• degree certificates, if already available

If you are still finishing a degree, unofficial transcripts are fine.

4. Evidence of Technical or Research Work

Please include at least one of the following if available:

• Master’s thesis abstract or summary
• thesis manuscript
• seminar paper
• publication or preprint
• project report
• GitHub profile or code sample
• technical portfolio

For research-oriented roles, evidence of how you think and work technically is often more informative than a long list of buzzwords.

5. Optional Supporting Information

This may include:

• names of references
• recommendation letters
• links to talks, posters, repositories, or demos
• a short research idea or topic suggestion
• your earliest possible start date
• whether you are applying for a funded position, thesis supervision, or a student assistant role

Please send your application by email with a clear subject line:

Application – [Position] – [Your Name]

Examples:

• Application – Student Assistant – Jane Doe
• Application – Master Thesis – Jane Doe
• Application – PhD – Jane Doe
• Application – Postdoc – Jane Doe

In the body of your email, you may also include a short summary of your strongest qualifications in 3–5 bullet points.

We do not evaluate applicants only by grades or formal labels. We look at the overall picture.

Important criteria include:

• Research Fit: Does the application clearly match the lab’s actual research themes?
• Technical Strength: Can the applicant implement, analyze, and reason carefully about technical systems?
• Scientific Potential: Does the applicant show evidence of curiosity, independence, and the ability to formulate or investigate meaningful questions?
• Depth, Not Just Breadth: We prefer a few strong projects or one excellent thesis over a long but shallow list of topics.
• Communication: Can the applicant explain their work clearly, both verbally and in writing?

For research positions, we especially value candidates who can connect theory, implementation, and empirical validation.

Below you find a rough outline of the application process. In many cases however, we might have more individual paths. Also, not every role requires every step.

Step 1: Initial Screening

We review the submitted material for fit to current lab research, academic background, technical skills, evidence of research or project quality, clarity and seriousness of motivation. Applications that are too generic or clearly outside our research focus are unlikely to move forward.

Step 2: Shortlisting

Shortlisted candidates may be invited to a first conversation with one of the lab members. Depending on the role, we may ask you in advance to share (e.g., a code sample, a thesis abstract, a publication, a short topic statement, a project you would like to discuss)

Step 3: Interview

The interview usually focuses on:

• your background and motivation
• one or two projects you have worked on in depth
• technical understanding
• problem-solving ability
• research fit with the lab

For PhD and postdoc candidates, we may also discuss:

• possible research directions
• how you approach open-ended questions
• your view on good scientific practice
• how you handle uncertainty, failure, and iteration in research

Step 4: Optional Follow-Up

Depending on the position, we may ask for one additional step, such as:

• a short presentation of your thesis or a project
• a deeper technical discussion
• a conversation with future collaborators in the lab
• a small technical exercise or reading-based discussion

Step 5: Decision:

After the process is complete, we will inform you of the outcome as soon as possible. We aim for a feedback within 14 days.

In the interview we want to check your expertise and we want to get an impressions on how you think. Hence, interviews will be a mixture of presentation, question, and coding.

Please be prepared to explain one of your projects in depth (for PhD candidates: the Master thesis)

• discuss design decisions and trade-offs
• describe what worked, what failed, and what you learned
• answer questions about methodology and implementation
• reason through unfamiliar but related technical questions

For machine learning / reinforcement learning positions, we may discuss topics such as:

• model design and evaluation
• overfitting, generalization, and data quality
• optimization and loss functions
• reinforcement learning fundamentals
• reproducibility and experimental rigor

For localization, sensing, or embedded ML topics, we may additionally discuss:

• sensor modalities and fusion
• uncertainty and robustness
• principles and foundations of wireless communications
• signal properties and preprocessing
• deployment constraints
• latency, memory, and energy trade-offs

We are generally less interested in polished buzzwords than in whether you can think clearly and honestly about technical questions and provide a solid understanding of the underlying concepts.