Overview
The Machine Learning Group at Rheinland-Pfälzische Technische Universität (RPTU) in Kaiserslautern, Germany, is seeking highly motivated candidates for PhD and postdoctoral positions in the field of Machine Learning and Deep Learning. This opportunity is significant for those passionate about advancing AI research, particularly in a region recognized as a key hub for artificial intelligence in Germany.
Background & Relevance
Machine learning is a rapidly evolving field that plays a crucial role in various domains, including healthcare, engineering, and computer vision. The RPTU’s Machine Learning Group is at the forefront of developing innovative deep learning techniques aimed at analyzing vast datasets. Their research encompasses areas such as anomaly detection, generative machine learning, and physics-informed deep learning, making substantial contributions to both theoretical and applied aspects of AI.
Key Details
- Positions Available: PhD and Postdoc
- Location: RPTU, Kaiserslautern, Germany
- Research Focus: Deep learning methods, anomaly detection, generative models, and more
- Contract Duration: Up to 48 months, mutually agreed upon
- Payment: Competitive German TVL E-13 scheme, based on experience
- Application Submission: Email to kloft[at]cs[dot]uni-kl[dot]de and fellenz[at]cs[dot]uni-kl[dot]de
- Subject Line: ‘Application for a [PhD|Postdoc] Position in the ML Group’
- Application Components: Cover letter, CV, list of referees, teaching and research statements
- Informal Inquiries: Contact Marius Kloft or Sophie Fellenz via email or in person at NeurIPS or EurIPS
Eligibility & Participation
Candidates for the PhD position should possess a Master’s degree in Computer Science with a focus on AI or a related quantitative field such as physics, statistics, or mathematics. For the postdoctoral role, applicants must have a PhD in computer science or a strong background in theoretical physics or mathematics, with a proven research publication record in the machine learning community.
Submission or Application Guidelines
To apply, candidates should prepare the following documents:
1. Cover letter (including signature)
2. Curriculum Vitae (including signature)
3. List of referees
4. Teaching and research statements
Applications should be sent via email to the specified addresses, ensuring the subject line is correctly formatted. There is no official deadline for submissions, as applications will be reviewed on an ongoing basis.
More Information
The research conducted at RPTU is not only foundational but also applicable across various sectors. The group actively engages in projects that span engineering, health, computer vision, text analysis, computational biology, and agriculture, showcasing the versatility and impact of machine learning methodologies. This environment provides a rich opportunity for collaboration and mentorship, particularly for PhD students seeking guidance from experienced researchers.
Conclusion
This is an excellent opportunity for individuals looking to advance their careers in machine learning within a dynamic research environment. Interested candidates are encouraged to apply and explore the potential for impactful research at RPTU. For further inquiries, reach out to Marius Kloft or Sophie Fellenz, and consider attending their presentations at upcoming conferences.
Category: PhD & Postdoc Positions
Tags: machine learning, deep learning, artificial intelligence, RPTU, Kaiserslautern, computer science, anomaly detection, generative models, physics-informed learning, data analysis, healthcare, computer vision, computational biology
