Fysiikka - Physics

The Educational Learning Modules on Li-ion batteries (ELMO-LION) is a 2 - 6 ETCS Credit Doctoral-level course to train the next generation of scientists and engineers in the interdisciplinary and rapidly accelerating field of Li-ion batteries. The course consists of three main pillars: (1) online lectures offered by experts (on battery fabrication, characterization, spectroscopy, simulations, and advanced diagnostics), (2) working cases and challenges from industrial partners to be solved by student teams and (3) a venture-creation summer school. Module "Online Lectures". Module includes 8 lectures for 2 hours each provided by famous experts of battery area (Synthesis of the battery materials, Spectroscopic study, First principles modelling, Diagnostic and operation, Reuse and recycling, Battery market), exercises and exam after lectures. Module combines fundamental theories and advanced technologies, conventional Li batteries and alternative electrochemical energy storage systems. In a case of successful completing this module (passing 50% of quizzes or more) student receives 2 credit certificate from LUT University.  Module "Project Work". Topics for projects are chosen from actual needs of industrial partners. Students must prepare ideas how to solve provided cases. Course includes supervision of mentors. In a case of successful completing of this module (passing all the assignments and writing report) student receives 2 credit certificate from LUT University.  Module "Summer School".It includes lectures and seminars from industrial and academic partners during the week of May 25. In Summer school, students continue project work in groups under industrial and academic supervising on industrial problem and should provide solution for given case, prepare report and apply proposal for Startup funding. In a case of successful completing of this module student receives 2 credit certificate from LUT University.

The Orientation Days activities. Practical study-related information, degree requirements. Planning of Master's studies. Preparation of the electronic personal study plan (PSP). Getting familiar with the support in monitoring the progress of the studies. Use of digital services in studies. The Academic Library collections and databases. Information security training.

This course offers a hands-on exploration of how modern particle physics experiments convert raw detector signals into quantitative physics results that push our understanding of the universe forward. By working directly with data—both simulated and real—from the CMS experiment at the CERN Large Hadron Collider (LHC), students will follow the entire analysis pipeline: from interpreting raw pulse shapes to reconstructing particle trajectories and performing high-level statistical tests. Participants will learn key techniques of signal processing, data calibration, and event reconstruction, developing critical computational and statistical skills that enable them to extract meaningful physics insights from complex datasets. Focusing on practical workflows, this course equips students to apply state-of-the-art analysis methods, culminating in final projects that mirror authentic research efforts in experimental particle physics. Graduates of the course will be prepared to tackle advanced topics in high-energy physics, data science, and other research-intensive fields that demand rigorous, data-driven approaches. Course Content Introduction to Experimental Particle Physics Data LHC experiment design and data acquisition Core detector technologies and their signals Signal Processing and Calibration Noise filtering, baseline correction, and time calibration Energy calibration and validation with known resonances Data Analysis Techniques Particle tracking, momentum reconstruction, and kinematic variables Calculating invariant masses and identifying decay products Statistical Tools for Particle Physics Likelihood methods, data fitting, and uncertainty quantification Significance testing and background estimation Working with CMS Data Accessing and manipulating real CMS datasets Event selection and categorization: leptons, jets, missing energy Hands-on case studies: reconstructing known particles (e.g., Z boson) and searching for new physics signatures Advanced Topics (Optional) Machine learning approaches for particle identification and classification Trigger systems and real-time data filtering Final Project Independent data analysis using CMS open data: from raw signals to final physics results Emphasis on clear documentation and robust statistical interpretations

This course offers a comprehensive introduction to the fundamental concepts of particle physics, bridging both theoretical foundations and experimental analysis. Students will explore the core principles of special relativity and quantum mechanics as they apply to particle interactions and decays, while gaining hands-on experience in data analysis using Python or similar programming languages. Topics include decay rates, cross-sections, the Dirac equation, Feynman diagrams, and deep inelastic scattering, providing a strong foundation for further studies in experimental and theoretical particle physics.Underlying concepts [special relativity, quantum mechanics] Decay rates and cross sections [Lorentz-invariance, matrix element] The Dirac equation [relativistic QM => spin + antimatter] Interaction by particle exchange [Feynman diagrams] Electron-positron annihilation [calculations in perturbation theory] Electron-proton elastic scattering [form factor] Deep inelastic scattering [Bjorken x, PDFs] (*Symmetries and the quark model)

This course explores the functional properties and applications of shape memory alloys (SMAs) and magnetic shape memory alloys (MSMAs), such as Ni-Ti and Ni-Mn-Ga alloy systems. Students will examine the microstructural features and phase transformations that enable key phenomena such as the shape memory effect, superelasticity, the magnetic shape memory effect, and caloric effects. The course covers SMA and MSMA behaviour, alloy design, material processing techniques, and the challenges of manufacturing and integrating these materials into functional devices, including the design of simple components. Examples of practical applications in fields such as biomedicine, aerospace, and robotics are also discussed. Instruction includes weekly lectures and quizzes, laboratory demonstrations, and an individual assignment (literature review and presentation to fellow students).

This course provides students with an opportunity to apply their previously acquired knowledge of characterisation techniques in an individual experimental project. Students will analyse metallic samples—either their own or those provided by the instructor—by first defining research objectives, selecting appropriate characterisation techniques, designing experimental procedures, preparing samples, conducting measurements, post-processing the experimental data, and critically interpreting the results. Throughout the course, students will work independently under the supervision of the teachers for guidance and feedback. Ultimately, each student writes a comprehensive technical report that includes methodology, results, discussion, and conclusions and presents their findings to their fellow students.

This course provides a basic-level understanding of key characterisation techniques (listed under ‘Learning Outcomes’) used to analyse the compositional, microstructural, and crystallographic properties of metals, including those produced through casting, laser additive manufacturing, or different single-crystal growth techniques. The course emphasises both theoretical knowledge and practical implementation, providing students with hands-on laboratory experience. Additionally, students will develop a theoretical foundation in studying metal microstructures, including phases, grain boundaries, crystallographic textures, and common defects. The course is structured into two-week modules, each focusing on a specific characterisation method. Each module includes a theoretical lecture, a quiz in Moodle, and a hands-on laboratory exercise where students apply the technique in practice. Based on their experiments, students collaborate in groups to write technical measurement reports, documenting and interpreting their results. A discussion session is held in the second week of each module to review findings before transitioning to the next topic. Furthermore, students will complete a small individual literature review on a selected characterisation method or its application, culminating in a presentation of their findings in a seminar session at the end of the course.

The students will learn basic properties of superconductivity, London equations, thermodynamics of the superconducting transition, intermediate state, coherence length, current in superconductor, thin films, BCS-theory, type-II superconductors, high-Tc superconductors, and some applications of superconductivity.

This master's-level course is intended to provide an overview of reliability aspects in detectors and microelectronics including radiation effects, addressing both theoretical foundations and practical implementations.

The goal of this advanced course is to give Master's students with a background in physics or a similar discipline a basic understanding of readout electronics and microelectronics, as well as their crucial significance in contemporary experimental physics. Students who complete the course will have the basic theoretical understanding and practical abilities needed to develop, estimate, and troubleshoot electronic systems that are used to measure and detect physical signals.

The theory, design, and applications of Solid State Detectors (SSDs), which are essential in many scientific and engineering fields, are examined in this advanced master's-level course. The course includes both theoretical principles and real-world applications.

Structure, operation and physics of semiconductor devices.

Mechanics part of the course: Basics of translational and rotational motion, Newton's laws, principles of conservation of energy, momentum and angular momentum. Thermal Physics: Physical basics of thermodynamics, the laws of thermodynamics, thermodynamic engines and cyclic processes. Electricity: Electrostatics (electric force, field and potential), direct-current circuits, magnetism (magnetic force and field), electromagnetic induction.

Lämpö-osuus: Lämpöopin fysikaaliset perusteet, termodynamiikan pääsäännöt sekä termodynaamiset laitteet ja kiertoprosessit. Sähkö-osuus: Sähköstatiikka (sähköinen voima, sähkökenttä, sähkökentän potentiaali), tasavirtapiirit, magnetismi (magneettinen voima, magneettikenttä), sähkömagneettinen induktio, vaihtovirtapiirien perusteet.

Lämpö-osuus: Lämpöopin fysikaaliset perusteet, termodynamiikan pääsäännöt sekä termodynaamiset laitteet ja kiertoprosessit. Sähkö-osuus: Sähköstatiikka (sähköinen voima, sähkökenttä, sähkökentän potentiaali), tasavirtapiirit, magnetismi (magneettinen voima, magneettikenttä), sähkömagneettinen induktio, vaihtovirtapiirien perusteet.

Mekaniikka-osuus: Etenevän ja pyörimisliikkeen perusteet, Newtonin lait, säilymislait (energia, liikemäärä ja liikemäärämomentti). Aaltoliike-osuus: Mekaaniset värähtelyt (harmoninen, vaimeneva, pakotettu), harmoninen aalto, mekaaniset ja sähkömagneettiset aallot, interferenssi, diffraktio, polarisaatio.

Mekaniikka-osuus: Etenevän ja pyörimisliikkeen perusteet, Newtonin lait, säilymislait (energia, liikemäärä ja liikemäärämomentti). Aaltoliike-osuus: Mekaaniset värähtelyt (harmoninen, vaimeneva, pakotettu), harmoninen aalto, mekaaniset ja sähkömagneettiset aallot, interferenssi, diffraktio, polarisaatio.