Hahn-Schickard — one of Germany’s leading research institutes for innovative microsystems technology, with a team of over 300 employees transforming ideas into cross-industry solutions from concept to manufacturing at three Baden-Württemberg locations — is seeking a Gruppenleitung Künstliche Intelligenz (m/w/d) at its Freiburg or Villingen-Schwenningen site. This is a permanent (unbefristete), senior research leadership position requiring a university degree and PhD (Promotion) in computer science, engineering, electrical engineering, or a closely related discipline, with demonstrable scientific publications and/or patents in AI. The role combines research group leadership, third-party funding acquisition (Drittmittelakquise), advanced ML model development, meta-learning research (federated learning, explainability), and integration into Hahn-Schickard’s strong research network with the University of Freiburg. Reference number: 26/1720/40.
What Hahn-Schickard Offers — Benefits, Working Conditions & Development
Contract: Unbefristete Stelle — permanent employment with attractive TV-L-aligned compensation at a modern, industry-connected research institute
Leave: 30 Urlaubstage per year + free days on 24 and 31 December + bridge days and working time account for Christmas/New Year shutdown — one of the most generous leave arrangements in German research
Academic Development: Possibility for Habilitation and Entrepreneurship training — supporting ambitious researchers who want to advance their academic standing or explore technology transfer and spin-off opportunities
Flexible Working: Flexible Arbeitszeiten + mobiles Arbeiten + Sabbaticals — genuine work-life flexibility beyond policy statement level
Wellbeing: Hansefit (epassi) premium fitness program · Corporate benefits and events · Zuschuss zu Mahlzeiten (meal subsidy)
Pension: Überdurchschnittliche Altersvorsorge — above-average employer pension provision
Mobility: Monatliche Zuschüsse for public transport and Jobrad (bicycle leasing) — supporting sustainable commuting
About Hahn-Schickard — Research Institute for Innovative Microsystems Technology
Organization: Hahn-Schickard — Non-profit research institute | 300+ employees | Three sites in Baden-Württemberg: Freiburg · Villingen-Schwenningen · Stuttgart
Research Focus: Mikrosystemtechnik (Microsystems Technology) and Informationstechnik (Information Technology) — from concept through to manufacturing, across industry-relevant application domains
AI at Hahn-Schickard: The AI research group works on ML models for time-series, video, and image data, task-agnostic to embedded models, meta-learning methods (federated learning, explainability), and collaborates closely with the Medical AI and Robotic AI groups
University Partnership: Strong integration into the research network of Hahn-Schickard and the University of Freiburg — one of Germany’s leading research universities, consistently ranked among the top institutions in Baden-Württemberg
Application: Submit complete application documents as a single PDF with reference number 26/1720/40 | Contact: Prof. Dr. Oliver Amft, +49 761 887865734
Why This Hahn-Schickard AI Group Leader Role Is a Career-Defining Opportunity in 2026
Research Leadership at the Frontier: Leading a dedicated AI research group at Hahn-Schickard — with P&L-equivalent responsibility for project acquisition, team leadership, scientific output, and industrial application — is one of the most comprehensive and career-defining senior AI research leadership appointments available at an applied research institute in Germany in 2026
Applied + Academic Balance: Hahn-Schickard’s model — applied research that is industry-proximate enough to attract direct industrial clients and Förderprojekte while being academically rigorous enough to produce scientific publications, supervise doctoral candidates, and connect deeply with the University of Freiburg — offers the rare career combination of real-world impact and scientific recognition that pure industry roles and purely academic positions each lack
Medical AI & GreenTech: Research focus areas — Medical AI, Robotic AI, Circular Economy (Kreislaufwirtschaft), and GreenTech — represent some of the most commercially significant, socially important, and professionally valued AI application domains in the 2026 research landscape
Freiburg Quality of Life: Freiburg im Breisgau is consistently ranked among Germany’s most liveable, most sustainable, and most culturally attractive cities — combining outstanding academic infrastructure, proximity to the Black Forest and Switzerland, and an exceptional quality of life for researchers and their families
Position Overview — AI Research Group Leader, Hahn-Schickard
This Gruppenleitung Künstliche Intelligenz (m/w/d) at Hahn-Schickard is a full-time, permanent senior research leadership position combining scientific team leadership, third-party project acquisition, advanced ML model development, meta-learning research, cross-group collaboration, doctoral supervision, and integration into the wider Hahn-Schickard and University of Freiburg research ecosystem. You will lead a research working group with multiple staff members and a portfolio of F&E projects covering AI innovations from idea to product; acquire Förderprojekte and direct industrial client projects; further develop and apply ML models for time series, video, and image data from task-agnostic to embedded deployments; advance meta-learning methods particularly in federated learning and explainability; collaborate closely with the Medical AI and Robotic AI groups and other Hahn-Schickard departments and partners; supervise students and doctoral candidates; and represent Hahn-Schickard’s AI capability in the external research network. The role requires a university degree and Promotion (PhD) in computer science, engineering, electrical engineering, or a related discipline, demonstrable scientific publication and/or patent track record in AI, strong leadership and project acquisition experience, proficiency in ML frameworks (TensorFlow, scikit-learn), and fluent German and English at negotiation level.
Why This Hahn-Schickard Gruppenleitung KI Role Is the Applied AI Research Career of 2026 in Baden-Württemberg: PhD-qualified AI and ML researchers with demonstrable scientific publication records, hands-on experience with TensorFlow or scikit-learn, specific expertise in meta-learning methods (federated learning and explainability), proven project leadership and third-party funding acquisition capability, the management skills to lead a multi-member research team across concurrent F&E projects, fluent German and English at negotiation level, and genuine interest in the intersection of AI with medical, robotic, industrial, and GreenTech applications — represent a rare and genuinely distinguished scientific talent profile that Hahn-Schickard’s offer of a permanent, TV-L-aligned, Habilitation-capable group leadership position at a leading Baden-Württemberg applied research institute is extremely well positioned to attract and retain.
Key Responsibilities — Ihre Aufgaben
Research Group Leadership & Project Portfolio Management
- Lead a research working group (Arbeitsgruppe) with multiple staff members across multiple concurrent F&E projects — providing the technical direction, organizational management, individual mentoring, and team motivation that sustains a high-performing, scientifically productive research group that consistently delivers innovative AI solutions from initial project concept through to final product-ready implementation, managing the project portfolio across the group’s AI domain responsibilities and ensuring that each project receives the scientific leadership, resource allocation, and milestone management that its deliverables and funding obligations require
- Acquire Förderprojekte (publicly funded research projects) and direct industrial client projects — identifying relevant national and EU funding calls (BMBF, BMWi, EU Horizon, DFG, and Baden-Württemberg state programs), developing compelling research proposals in collaboration with academic and industrial partners, managing the administrative and scientific aspects of successful funding applications, and directly acquiring project commissions from industrial clients who want Hahn-Schickard’s AI engineering expertise applied to their specific product or process development challenges
- Supervise students and doctoral candidates (Doktoranden) — providing the scientific mentorship, research guidance, methodological training, and professional development support that master’s students and PhD researchers working within the AI group need to produce high-quality research contributions, complete their theses to schedule, and develop into independently capable researchers and engineers in the AI field
- Represent Hahn-Schickard’s AI research in the external scientific and industrial community — publishing research results in peer-reviewed journals and conferences, presenting at scientific symposia, representing Hahn-Schickard in consortium and partnership meetings with research and industrial partners, and building the external professional reputation that attracts talented researchers, industrial clients, and competitive funding applications to the AI group
Advanced ML Model Development — Time Series, Video, Image & Embedded AI
- Further develop and apply ML models for time-series, video, and image data — advancing the AI group’s capability in the specific ML model architectures (RNNs, LSTMs, Transformers for time-series; CNNs, Vision Transformers for image and video; multi-modal fusion architectures combining modalities) that are most relevant to Hahn-Schickard’s industrial, medical, and robotic AI application domains, developing models that achieve the accuracy, robustness, and generalization performance that real-world industrial deployment requires rather than only the benchmark performance that academic publication demands
- Develop task-agnostic through to embedded AI models — spanning the full deployment spectrum from large, general-purpose foundation models and task-agnostic ML architectures that perform well across diverse application contexts, through to highly optimized, quantized, and resource-constrained embedded AI models that operate within the tight compute, memory, and power budgets of the microcontrollers and edge hardware platforms that Hahn-Schickard’s microsystems technology products use
- Work with TensorFlow, scikit-learn, and other leading ML frameworks — applying expert-level proficiency in the specific ML development tools, training infrastructure, model evaluation methodology, and deployment pipelines that professional AI engineering at a leading research institute requires, and maintaining current knowledge of the rapidly evolving ML framework ecosystem to identify and adopt new tools that improve the research group’s productivity and technical capability
Meta-Learning Research — Federated Learning & Explainability
- Advance meta-learning methods with particular focus on federated learning (föderiertes Lernen) — developing the research group’s expertise and output in federated learning architectures that train ML models across distributed data sources without centralizing sensitive data, applying this particularly to the medical AI domain where patient data privacy constraints make federated learning a practically essential rather than merely academically interesting approach to building high-performance ML models for clinical decision support and medical device applications
- Advance explainability (Erklärbarkeit) research for AI systems — developing model-agnostic and model-specific explainability methods (SHAP, LIME, attention visualization, concept-based explanations, counterfactual explanations) that make AI model decisions interpretable, auditable, and trustworthy for the industrial, medical, and regulatory stakeholders who need to understand and validate AI system behavior before deployment in safety-critical applications
- Integrate meta-learning research with practical application requirements — ensuring that federated learning and explainability research advances are translated into practical tools, methodologies, and implementations that the AI group’s industrial and medical partners can actually use in their product and process development, maintaining the tight coupling between fundamental research contribution and applied technology transfer that distinguishes Hahn-Schickard’s research approach from purely academic work
Cross-Group Collaboration — Medical AI, Robotic AI & Industrial Applications
- Collaborate closely with the Medical AI and Robotic AI groups at Hahn-Schickard — contributing the AI group’s specific ML and meta-learning expertise to joint research projects that combine the Medical AI group’s clinical domain knowledge and regulatory expertise with the Robotic AI group’s perception and control system knowledge, creating the cross-disciplinary AI research capability that solves complex applied problems that no single group could address independently
- Engage with AI applications in industry, Kreislaufwirtschaft (circular economy), and GreenTech — applying ML and meta-learning methods to the specific AI use cases that emerge from industrial manufacturing process optimization, quality control, predictive maintenance, circular economy material sorting and lifecycle tracking, and environmental monitoring applications that represent strategically important growth areas for Hahn-Schickard’s research portfolio
- Integrate into the research network of Hahn-Schickard and the University of Freiburg — actively participating in joint seminars, collaborative research projects, shared infrastructure, and the informal scientific exchange that the Hahn-Schickard/University of Freiburg research ecosystem makes available to group leaders, leveraging this network to access academic talent, shared expertise, and collaborative funding opportunities that enrich the AI group’s scientific output and professional standing
Qualifications & Application
- Completed university degree (Hochschulabschluss) AND Promotion (PhD/Doktortitel) — in Informatik, Ingenieurswissenschaften, Elektrotechnik, or a closely related scientific or technical discipline that provides the theoretical depth and research methodology foundations for leading an applied AI research group
- Relevant scientific experience demonstrated through peer-reviewed publications and/or patents in AI — the formal scientific track record that validates the candidate’s capability to lead and produce high-quality AI research at Hahn-Schickard’s level
- Ideally combined with relevant industrial experience in AI — demonstrating the practical, application-oriented perspective that distinguishes applied research institute group leaders from purely academic researchers
- Strong communication and persuasive presentation ability — for scientific seminars, partner meetings, funding proposal presentations, and team leadership interactions
- Experience in project leadership and acquisition — proven capability to define, win, and deliver funded research projects and direct industrial commissions
About Hahn-Schickard & Applied AI Research Leadership in Freiburg 2026
Hahn-Schickard‘s position at the intersection of academic scientific rigor and direct industrial application — researching and developing microsystems and AI solutions that move from concept through to manufactured product rather than stopping at publication — makes it one of the most professionally rewarding environments for AI researchers who want their work to matter beyond the conference paper. The Gruppenleitung KI position offered here is genuinely rare in the German applied research landscape: a permanent leadership appointment with Habilitation possibility, substantial project acquisition and team leadership responsibility, research focus areas of exceptional current commercial and scientific relevance (federated learning, explainability, medical AI, robotic AI, GreenTech), strong institutional support including University of Freiburg integration, and the personal quality-of-life environment of one of Germany’s most consistently admired cities. For PhD-qualified AI researchers who are ready to step into this kind of leadership — technically demanding, scientifically rigorous, industrially impactful, and personally fulfilling — this is the appointment to pursue.
Your Career Growth Path: Gruppenleitung KI → Senior Gruppenleitung / Abteilungsleitung → Bereichsleitung AI → Wissenschaftlicher Direktor → Habilitation / Entrepreneurship — a research leadership career trajectory built at the frontier of applied AI in one of Germany’s most technically distinguished and personally rewarding research environments.
Who Should Apply?
- Senior AI/ML Researchers — Post-Doc or Junior Group Leader Level: With completed PhD, demonstrable publication record, TensorFlow/scikit-learn expertise, and federated learning or explainability research experience who want a permanent group leadership position at a leading Baden-Württemberg applied research institute
- AI Research Engineers — Industrial + Academic Background: Who combine practical ML engineering experience from industry with the scientific publication track record and academic credentials that an applied research group leadership role at Hahn-Schickard requires
- Medical AI or Robotic AI Researchers — Federated Learning Expertise: With specific federated learning implementation and research experience in privacy-sensitive healthcare or robotic AI contexts who want to lead a dedicated AI research group that works closely with Hahn-Schickard’s Medical AI and Robotic AI teams
- University Research Group Leaders — Applied Research Transition: Currently leading a research group at a German university who want to move into applied research with stronger industrial connection, more direct technology transfer opportunity, and the security of an unbefristet research institute position
- International AI Researchers — Germany Relocation: With PhD qualifications, strong publication records, and TensorFlow/meta-learning expertise who want to build their research leadership career in Germany at an internationally connected, technically excellent, and personally supportive applied research institute in one of Germany’s most attractive cities
