Micro-credentials

Composed of theoretical content and practical assignments the course concentrates on providing students with skills related to identifying and planning personal or professional goals as well as using selected time management and project management tools to achieve these goals. The course derives from a holistic and systemic approach to skills development.

The course is composed of 4 themes divided into smaller study units:

1. What skills do we need in todays’ world?
2. The role of values in skills development.
3. Your personal or professional goal as a project.
4. Self-management and project management tools.

Learning objectives:

Students are able to:

• define techniques and methods for proactive behavior,
• identify personal needs and areas of learning,
• assess their own level of development,
• create individual learning and development goals,
• identify techniques and methods of self-reflection.

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This practical laboratory course provides a comprehensive introduction to computational chemistry and is specifically designed for chemistry-related or closely associated students with no prior experience in computations. Participants will gain a broad understanding of the fundamental methodologies and applications of computational chemistry, beginning with essential technical skills in a Unix/Linux environment and progressing to the management of scientific simulations on High-Performance Computing (HPC) clusters.

• Tools of computational chemistry: Linux terminal, text editors, HPC environments, crystal structure databases, and visualization programs,
• Quantum Mechanics (QM) calculations: Geometry optimization, energy analysis, UV spectra, HOMO-LUMO orbitals using Gaussian
• Molecular Dynamics (MD) simulations: Force fields, system preparation, alanine dipeptide and protein-ligand complex simulations using AMBER
• Introduction to QM/MM and other computational methods
• Small molecules and protein systems visualization programs (pyMOL, VMD, Gaussian).
• Data analysis and interpretation

Learning objectives:

Students are able to:

• Digital/Data Literacy (list and use programming languages and data visualization tools, configure connections in the digital space): Students are able to apply specialized command-line tools and text editors in a Unix-like environment to manage and configure scientific data workflows on High-Performance Computing (HPC) clusters,
• Critical/Systems Thinking (recognize facts, concepts, theories and principles, student can to carry out an informed, critical analysis): Students are able to recognize and evaluate the core concepts and principles of QM, MM, and QM/MM methodologies to carry out an informed, critical analysis of their applicability to complex chemical systems,
• Data Literacy/Critical Thinking (collect, classify and evaluate data, analyze information to reach factually sound conclusions): Students are able to collect, classify, and evaluate computational data from Quantum Mechanics (QM) calculations to analyze complex information and reach factually sound conclusions regarding molecular electronic properties and spectra,
• Problem Solving (evaluate information objectively and form well-reasoned conclusions): Students are able to conduct Molecular Dynamics simulations and interpret molecular trajectories by objectively evaluating structural parameters (RMSD, distances) to form well-reasoned conclusions about biomolecular stability,
• Digital Collaboration (exchange information in the digital space and collaborate with the help of digital tools): Students are able to collaborate effectively in the digital space by exchanging simulation data and leveraging digital tools to achieve a common research goal in a hybrid learning environment.

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The course provides students with a comprehensive understanding of how AI technologies are reshaping clinical decision-making, diagnostics, personalized medicine, and healthcare management. At the same time, it critically examines the legal and ethical implications associated with the use of large-scale health datasets, algorithmic decision systems, and digital health platforms.

The program begins with an introduction to the technological foundations of AI in medicine, including machine learning applications in clinical practice and biomedical research. It then moves to the analysis of health data ecosystems, addressing issues such as data collection, interoperability, data sharing infrastructures, and the governance of medical information in research and clinical contexts.

A significant part of the course is devoted to the regulatory landscape governing AI and health data in Europe, including the legal principles of data protection, medical data processing, and digital health regulation under European law. Particular attention is paid to the implications of the European Health Data Space (EHDS) and the emerging framework established by the European Union Artificial Intelligence Act (AIA) for medical AI systems.

The course also explores the ethical frontiers of AI-driven medicine, including issues of algorithmic transparency, accountability in automated decision-making, bias in medical datasets, patient autonomy, and the impact of predictive technologies on the doctor-patient relationship. Through interdisciplinary dialogue between legal scholars, economists, and medical experts, students will develop a critical perspective on the governance of digital health technologies. The course is designed for students and young researchers interested in digital health, health law, medical innovation, and data governance, and aims to foster interdisciplinary competencies necessary to navigate the rapidly evolving landscape of AI-enabled healthcare.

Learning outcomes:

By the end of the course, students will be able to:

• Understand the technological foundations and principal applications of artificial intelligence in contemporary medical practice and biomedical research.
• Analyse the role of health data in AI-driven medicine and the main models of data governance in healthcare systems.
• Identify the legal frameworks regulating the processing and use of medical data in the European context, with particular reference to the European Health Data Space Regulation.
• Evaluate the regulatory implications of AI-based medical technologies under emerging European legislation, including the European Union Artificial Intelligence Act.
• Assess ethical challenges related to algorithmic decision-making in medicine, including transparency, bias, accountability, and patient autonomy.
• Critically examine the impact of AI systems on the doctor–patient relationship and on clinical responsibility.
• Interpret and discuss case studies concerning the deployment of AI tools in diagnostics, predictive medicine, and healthcare management.
• Develop interdisciplinary analytical skills integrating legal, ethical, economic, and medical perspectives.
• Formulate informed evaluations of regulatory and governance models for digital health technologies.
• Engage in academic and policy-oriented debates on the future development of AI-driven healthcare systems in Europe.

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