Finde einen Job 
Finde eine Ausbildung 
Top Arbeitgeber 
Top Universitäten 
Für Unternehmen Auf einen Blick
- Aufgaben: Dive into cutting-edge research on Tabular Foundation Models and collaborate with top experts.
- Arbeitgeber: Join the University of Freiburg, a leader in AutoML and innovative research.
- Mitarbeitervorteile: Enjoy a stimulating academic environment with access to advanced resources and technology.
- Warum dieser Job: Be part of groundbreaking research that impacts data science and machine learning applications.
- Gewünschte Qualifikationen: Bring your deep learning skills, Python expertise, and passion for data science.
- Andere Informationen: Work closely with @Prior Labs, the first startup focused on tabular foundation models.
Das voraussichtliche Gehalt liegt zwischen 36000 - 60000 € pro Jahr.
PhD Student on Tabular Foundation Models
Role Description
This is a full-time on-site role for PhD Students on Tabular Foundation Models at the University of Freiburg. The role involves conducting research on various topics related to TabPFN, such as:
- explainability of model predictions
- mechanistic interpretability
- scaling up to larger datasets
- time series
- relational data
- inference speedups
- causal discovery
- data science assistants
- automated data science
- applications of TabPFN in science.
PhD Students will be immersed in a stimulating academic environment that fosters growth and learning, and will have the opportunity to collaborate closely with @Prior Labs, the world’s first startup on tabular foundation models.
The ML Freiburg group
ML Freiburg is amongst the leading groups in AutoML, with Frank being the world’s most cited researcher in AutoML. The group has won the first two international AutoML challenges (2015-2016 and 2017-2018), with continuously improving versions of its widely-used open-source tool Auto-sklearn. Recently, it proposed the radically different approach of prior-data-fitted networks, which led to the tabular foundation model TabPFN (v1 and v2). The group owns over 200 GPUs and regularly participates in large-scale compute proposals on super computers.
Qualifications
Hands-on:
- experience in deep learning
- Proficiency in Python and PyTorch
- Knowledge of tabular data structures and machine learning algorithms
Knowledge of data science and statistical modeling:
- Strong problem-solving and analytical skills
- Ability to work independently and as part of a research team
- Excellent written and verbal communication skills
- Background in computer science, statistics, mathematics, or a related field
- Publication record or research experience in tabular data
Seniority Level: Internship
Employment Type: Full-time
#J-18808-Ljbffr
PhD Student on Tabular Foundation Models Arbeitgeber: ELLIS Unit Freiburg
Kontaktperson:
ELLIS Unit Freiburg HR Team
StudySmarter Bewerbungstipps 🤫
So bekommst du den Job: PhD Student on Tabular Foundation Models
✨Tip Number 1
Make sure to familiarize yourself with the latest research and developments in tabular foundation models, especially those related to TabPFN. This will not only help you understand the field better but also show your genuine interest during discussions.
✨Tip Number 2
Engage with the ML Freiburg group’s work by exploring their publications and open-source tools like Auto-sklearn. Being knowledgeable about their projects can give you an edge in conversations and demonstrate your proactive approach.
✨Tip Number 3
Network with current PhD students or researchers in the field, especially those associated with the University of Freiburg. They can provide valuable insights into the application process and what the team is looking for in a candidate.
✨Tip Number 4
Prepare to discuss your hands-on experience with deep learning, Python, and PyTorch in detail. Be ready to share specific examples of projects you've worked on that relate to tabular data structures and machine learning algorithms.
Diese Fähigkeiten machen dich zur top Bewerber*in für die Stelle: PhD Student on Tabular Foundation Models
Tipps für deine Bewerbung 🫡
Understand the Role: Make sure to thoroughly read the job description for the PhD Student position. Familiarize yourself with the specific research topics mentioned, such as explainability of model predictions and causal discovery, to tailor your application accordingly.
Highlight Relevant Experience: In your CV and cover letter, emphasize any hands-on experience you have in deep learning, Python, and PyTorch. Mention any projects or research that involved tabular data structures and machine learning algorithms.
Showcase Your Skills: Demonstrate your problem-solving and analytical skills in your application. Provide examples of how you've worked independently and as part of a research team, and highlight your communication skills through clear and concise writing.
Prepare a Strong Cover Letter: Craft a compelling cover letter that explains your motivation for applying to this position at the University of Freiburg. Discuss your interest in the ML Freiburg group and how your background aligns with their research focus on tabular foundation models.
Wie du dich auf ein Vorstellungsgespräch bei ELLIS Unit Freiburg vorbereitest
✨Showcase Your Research Experience
Be prepared to discuss your previous research projects, especially those related to tabular data or machine learning. Highlight any publications or significant findings that demonstrate your expertise and passion for the field.
✨Demonstrate Technical Proficiency
Since the role requires hands-on experience in deep learning and proficiency in Python and PyTorch, be ready to discuss specific projects where you utilized these technologies. Consider sharing code snippets or discussing challenges you overcame during implementation.
✨Understand the Group's Work
Familiarize yourself with the ML Freiburg group's research, particularly their work on AutoML and TabPFN. Being able to reference their projects and express how your interests align with theirs will show your genuine enthusiasm for joining their team.
✨Prepare Questions About Collaboration
Since the role involves collaboration with @Prior Labs, think of insightful questions about potential joint projects or how interdisciplinary teamwork is fostered. This shows your eagerness to engage and contribute to a collaborative research environment.
