Core állások, munkák oktatás, tudomány, sport területen

• Leading the Agile Transformation process: building, increasing and sustain maturity of high-performing self-organized Agile teams
• Promote and advocate a culture of continuous improvement, setting goals and milestones

• Researching trends and innovative methods for transformation, AI and global learning & development
• Supporting the identification and documentation of concrete AI use cases, agents, and adoption opportunities in learning, leadership, and HR contexts

Postdoc position: Chemist for selective ion scavenging in batteries Postdoc position: Chemist for selective ion scavenging in batteries Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of...

• Durchführung und Leitung von abwechslungsreichen Group Fitness Lektionen (indoor und outdoor) gemäss Kursplan – idealerweise mit Erfahrung in verschiedenen Formaten.
• Organisation und Betreuung von Personal Trainings inkl. individueller Beratung und Trainingsbegleitung

• Develop and adapt bioinformatics approaches for the analysis of human genomic and epigenomic data sets
• Design and implement analysis workflows for the analysis of long-read whole-genome sequencing and targeted short-read sequencing (hybridization capture, MeDIP-seq) data

Scientific Intern and Lab Support (100% - for 12 months) We are offering an exciting, hands-on internship for a motivated student eager to gain practical experience in a dynamic R&D environment. As a Scientific Intern and Lab Support at CDR-Life, you will contribute to both research activities and...

• USP process development and optimization using various bioreactor systems (Sartorius, Finesse, Cytiva)
• Transfer of processes to the GMP environment
• Support of general laboratory organization
• Participation in weekend duties as required
• Regular reporting in the form of written reports and presentations

• Developing multi-scale and multi-modal representation learning methods for scientific imaging data (e.g., SEM, TEM, EBSD).
• Learning representations that are robust to scale changes, modality shifts, and domain differences across instruments and laboratories.