LUT University Applied Physics Master's Thesis Guide: FY30A1200, 30 ECTS, Seminar and LUTPub

Quick answer: what is the LUT University Applied Physics thesis route?

The current standard Master’s Programme in Applied Physics at LUT University is a 120 ECTS Master of Science in Technology programme in the LUT School of Engineering Sciences. For the current 2026-2027 FyDAPhy_KJ curriculum, the exact thesis object is FY30A1200 Master’s Thesis and Seminar, 30 ECTS. It is graded 0-5, and the current Sisu course criterion states Master’s thesis 100%. The seminar is already built into FY30A1200: students present their thesis work, give a short presentation of results, and discuss/review presentations with other thesis students and instructors. The curriculum separately contains LUTKYPSYT Maturity test in Master’s degree, 0 ECTS.

1. Start from the standard 2026-2027 FyDAPhy_KJ curriculum

Use the current standard Applied Physics curriculum rather than copying a structure from another LUT programme. LUT also has a separate Applied Physics POLIMI double-degree curriculum, but that is a different degree arrangement. This guide uses the normal FyDAPhy_KJ object. The distinction matters because thesis packaging, transfer studies and degree structure can differ in a double-degree route even when the programme name looks similar.

2. The formal degree is 120 ECTS

The Applied Physics degree is a 120 ECTS Master of Science in Technology designed for two years of full-time study. The current standard curriculum includes at least 85 ECTS of Advanced specialisation studies and at least 20 ECTS of Minor Studies. The thesis belongs to the advanced part of the degree. Always plan from your own Sisu/HOPS structure rather than assuming every course visible in the programme must be taken by every student.

3. FY30A1200 is the exact thesis course

The current course is FY30A1200 Master’s Thesis and Seminar, 30 ECTS. Sisu classifies it as an advanced-studies Master’s-thesis course. Its learning outcomes emphasise applying scientific knowledge and methods in Applied Physics, working independently, preparing a research plan, completing the designed research and reporting according to scientific-writing principles. This is the controlling programme-specific thesis object, not a generic LUT thesis description borrowed from another degree.

4. The thesis is graded 0-5 and the thesis itself is 100% of FY30A1200

The current FY30A1200 evaluation criterion is unusually clear: 0-5, Master’s thesis 100%. The seminar activity is part of the course completion process, but the current public course object does not assign a separate weighted seminar percentage or separate seminar ECTS. Do not invent a seminar grade component from another LUT programme. University-level thesis assessment rules still apply, including examiner statements and Dean approval.

5. The seminar is embedded inside the 30 ECTS thesis object

FY30A1200 explicitly says that the thesis work is presented in a seminar with other thesis students and their instructors. The student gives a short presentation on project results and the presentations are discussed and reviewed. This means Applied Physics has a real seminar requirement, but the current evidence packages it inside FY30A1200. Do not count an extra seminar course unless your own current study plan explicitly shows one.

6. Treat the workload as a major six-month research project

The FY30A1200 completion method allocates 410 hours to research work, 200 hours to independent study and 200 hours to report preparation. Current eLUT guidance also describes the Master’s thesis as approximately six months of full-time work. The practical message is that the thesis should not be planned as a short final report written after experiments. Literature work, research design, setup, analysis, supervisor feedback, writing and revision need to be scheduled from the beginning.

7. A research thesis and an implementation thesis are both possible

FY30A1200 describes the thesis as either a research project or an implementation project. In both cases, the report needs a scientific structure: define the problem and context, explain the methods, describe the actual analysis or implementation actions, present results, and evaluate the outcomes and conclusions. Building a detector circuit, simulation pipeline or prototype is therefore not enough by itself. The thesis must show what question the work answers and how the evidence supports the conclusion.

8. Freeze the research question before the technical work becomes too large

Applied Physics projects can easily expand because there are many possible measurements, simulations or hardware variations. Write one main research question and, where needed, a small number of subquestions. Then connect every major experiment or simulation to one of those questions. This prevents a common problem where the student produces a large amount of technical data but cannot explain which result is actually needed for the thesis argument.

9. Build the research plan with the supervisor before committing to the setup

The thesis course expects the student to prepare a research plan and work independently while keeping contact with the supervisor. Before ordering components, booking instruments or running long simulations, define the question, main variables, samples/devices, comparison conditions, expected outputs, analysis method and decision rule. Also identify what can fail. A plan that includes fallback evidence is much safer than depending on one instrument, one sample or one simulation configuration.

10. Know the primary and secondary supervisor rules

LUT’s current Degree Regulations set formal supervisor qualifications. The primary supervisor normally holds a doctorate and belongs to the specified LUT professor, tenure-track, docent or qualifying employee categories, and normally comes from the student’s own degree programme. The primary supervisor confirms a secondary supervisor. The secondary supervisor must have at least a university Master’s degree and does not have to be an LUT employee, which is useful for company or external research projects.

11. Formal topic approval has prerequisites and a validity period

The topic is agreed between the student and primary supervisor and approved by the primary supervisor. Current LUT rules state that the Bachelor’s degree and possible supplementary studies must be completed before formal topic approval. Once approved, the topic remains valid for two years. If the project direction changes substantially, do not assume the original approval automatically covers a new research problem; discuss whether the approved topic or administrative record needs updating.

12. Examiners propose the grade and the Dean approves the assessment

The examiners provide a collective statement and propose the thesis grade. The Dean approves the assessment. If examiners propose different grades, the evaluation goes to the academic council and a third examiner statement may be requested. An approved Master’s thesis cannot simply be rewritten later to raise the grade. This makes the pre-submission review important: resolve known weaknesses before the thesis enters formal examination.

13. LUTKYPSYT is a separate 0 ECTS maturity requirement

The current Applied Physics curriculum lists LUTKYPSYT, 0 ECTS. LUT’s rules require a Master’s maturity test demonstrating knowledge of the thesis topic. University guidance allows the maturity test to be part of the thesis or closely related text and to be completed through a thesis seminar/equivalent, with an Exam route possible according to programme instructions. The abstract may serve as the maturity test, but current public Physics evidence does not justify saying that this is the only possible route for every student.

14. Confirm your individual maturity-test route before final submission

In an English-language degree programme, the maturity test is generally completed in English unless individual Finnish or Swedish language-demonstration rules apply. The primary supervisor evaluates the Master’s maturity test pass/fail. Because the precise route can depend on a student’s previous education and programme instructions, confirm it with the supervisor or current eLUT guidance rather than relying on a friend’s process from another programme or intake.

15. Understand the Form 1A to LUTPub submission chain

The current operational process begins when the student sends Form 1A to LUT Graduation. After processing, the first examiner confirms the topic and second examiner. When the thesis is ready, the student sends it to the first examiner and submits Form 1B plus the abstract to LUT Graduation. The examiners assess the thesis in HOTT, LUT Graduation prepares the Dean’s list, the Dean approves the assessment, the assessment moves to Sisu, and the student then uploads the thesis to LUTPub for library processing.

16. Recheck the live eLUT process when you are ready to submit

Curriculum facts such as FY30A1200 being 30 ECTS are relatively stable within the curriculum period, but operational forms, email addresses and processing steps can change. Before submitting, reopen the current eLUT Master’s thesis page and follow the current instructions. Do not rely only on a saved PDF, an old student screenshot or a guide written for a previous academic year.

17. The final thesis is a public document

LUT Master’s theses are public. The public thesis and its annexes must not contain confidential information. A company project can use confidential background material, but the public report has to be written so protected information is not disclosed. Publication in LUTPub can be delayed for up to two years in the permitted process, but the thesis remains a public document and can still be available on request through the library.

18. Solve company confidentiality before writing the final results chapter

If a company supplies device specifications, manufacturing details, proprietary data or unreleased performance information, decide early what can appear in the public thesis. Separate confidential working material from the public evidence chain. Use ranges, anonymised identifiers, public specifications, aggregated results or other approved representations where scientifically defensible. Do not wait until the final week to discover that the central figure cannot legally be published.

19. Turnitin checks originality, not whether the physics is correct

All LUT theses are checked using an electronic plagiarism detector, and the current process uses Turnitin. The supervisor interprets the report. A low similarity score does not validate a detector calibration, simulation model, uncertainty calculation or materials measurement. Treat originality, responsible citation and scientific validity as separate checks. A technically wrong result can still have low similarity, and a properly quoted methods passage can still produce a visible match.

20. Use AI only as support and keep the scientific responsibility yourself

Current LUT thesis guidance allows AI as a support tool but not as a substitute that produces the entire thesis or complete sections in place of the student’s work. Check AI-assisted code, equations, references, interpretations and prose against primary sources and actual execution. Do not enter confidential project material, personal data or research material containing personal information into AI tools. If AI is used in a way that affects research data or analysis, follow the current LUT disclosure and participant-information rules.

21. Numerical-simulation theses need more than a software screenshot

BM20A9001 Numerical Simulation covers linear systems, ODEs/DAEs, optimisation, parameter estimation and statistical analysis. A strong simulation thesis therefore reports the physical model, equations, boundary or initial conditions, numerical method, solver settings and parameters. Explain why the model is suitable for the research question. A colourful MATLAB plot is an output, not yet evidence, until the assumptions and validation route are clear.

22. Check convergence, sensitivity and parameter uncertainty when they matter

Numerical conclusions can change with time step, mesh, tolerances, initial values or fitted parameters. Where these choices can materially affect the claim, run convergence or sensitivity checks and report them. BM20A9001 also covers parameter-estimation statistics, covariance, cross-validation and bootstrap/noise-based approaches. Use the method that fits the model and data rather than reporting one fitted curve as if the estimated parameters were exact.

23. Probabilistic simulation should show variability, not one lucky run

BM20A8501 Probabilistic Simulation covers random-event generation, statistical/empirical distributions, discrete-event models, stochastic differential equations and practical simulation. State the random-variable assumptions, distribution choices and number of repetitions. Summarise the distribution of outcomes or uncertainty interval where relevant. One stochastic trajectory can illustrate behaviour, but it should not be treated as the expected performance of the system unless the design justifies that interpretation.

24. Separate model uncertainty from numerical randomness

A probabilistic result can vary because of random sampling, uncertain parameters, model assumptions and numerical approximation. These are not the same source of uncertainty. Where possible, state which uncertainty is being estimated and which is held fixed. If a conclusion depends strongly on one distributional assumption, show that dependence rather than presenting the result as a universal property of the physical system.

25. Semiconductor-device work must be tied to structure and operating conditions

FY30A0200 Physics of Semiconductor Devices provides the programme’s semiconductor-device physics foundation. When a thesis studies a device, describe the relevant structure, material system, geometry, biasing or operating condition and the physical mechanism behind the measured or simulated response. Avoid broad statements such as “the device performs better” without defining what metric improved and under which conditions.

26. Detector performance needs calibration and a defined metric

FY30A0300 Solid State Detectors and Their Applications focuses on detector physics, design considerations and applications. A detector thesis should define the performance quantity: efficiency, gain, energy/timing resolution, noise, linearity, stability, radiation tolerance or another metric. Report calibration procedure and operating conditions. If two detectors are compared, keep the measurement conditions sufficiently comparable to support the claimed difference.

27. Keep simulated electronics and measured electronics separate

FY30A0400 Microelectronics and Readout Electronics for Experimental Physics includes detector operation, signal processing, readout-circuit design, circuit simulation and hands-on systems. If a thesis uses both simulation and a physical circuit, label them clearly. State the component models and simulation assumptions, then explain the measurement setup and instrumentation for the hardware result. Agreement between simulation and experiment is evidence only when both sides are defined well enough to compare.

28. Reliability claims need a stress condition and failure definition

FY30A0500 Reliability of Detectors and Microelectronics covers reliability engineering, performance under different conditions, robust circuits, testing and radiation effects. Define what counts as degradation or failure, the exposure or stress condition, sample size and observation duration. A short accelerated test does not directly prove a long service lifetime unless the extrapolation model and assumptions are justified. Distinguish observed behaviour from predicted lifetime.

29. Superconductivity work should connect theory, sample state and measurement

FY30A0600 Superconductor Physics covers London equations, thermodynamics of the superconducting transition, coherence length, thin films, BCS theory, type-II and high-Tc superconductors. If experimental data are compared with theory, report the sample/material condition and measurement context. A mismatch may reflect model assumptions, sample quality, geometry or measurement limitations rather than simply showing that the theory is wrong.

30. Choose materials-characterisation tools from the research question

FY30A0700 Foundations of Materials Characterisation covers EDS, XRF, optical microscopy, SEM, AFM/MFM, XRD and EBSD and emphasises advantages, limitations and suitability. Do not choose an instrument only because it is available. Start from the quantity you need to resolve: composition, phase, crystal structure, texture, grain orientation, surface morphology or another feature. Then justify why the selected technique can answer that question at the required scale and accuracy.

31. Record sample preparation and acquisition settings

Materials results can depend strongly on polishing, etching, coating, section orientation, beam energy, scan area, detector settings, calibration standards or data-processing choices. Preserve these details in the methods or an appendix where appropriate. If samples were excluded because of preparation failure or measurement quality, record the rule transparently. Otherwise the final micrograph or diffraction pattern becomes difficult to interpret or reproduce.

32. Multiple characterisation techniques should have a defined relationship

A thesis may combine SEM/EDS, XRD, EBSD, AFM or other methods. Explain whether the techniques are intended to corroborate the same claim, answer different parts of the question, or resolve a disagreement. Do not treat every instrument output as independent confirmation when the techniques share the same sample preparation, spatial region or underlying assumption. Where results conflict, investigate the mismatch instead of selecting only the convenient method.

33. Use FY30A0800 as a model for a disciplined experimental project

FY30A0800 Project work in Materials Characterisation explicitly moves from research objectives to technique selection, sample preparation, measurements, post-processing, interpretation, a technical report and oral presentation. That sequence is a useful thesis model. Keep a decision log showing why methods changed, what measurements failed, and how the final evidence set was selected. This makes the discussion more credible than presenting the final workflow as if it worked perfectly from the first attempt.

34. Shape-memory-alloy results depend on composition and history

FY30A0900 Shape Memory Alloys and Their Applications connects functional behaviour to phase transformations, microstructure, processing and alloy design. Report composition, processing or heat-treatment history and relevant test conditions before comparing transformation temperature, strain recovery, superelasticity or other behaviour. A result from one specimen should not automatically be generalised to the entire alloy family without evidence supporting that scope.

35. Particle-physics work must separate theory, detector observables and reconstruction

FY30A1000 Introduction to Particle Physics combines theoretical calculations with computer analysis of experimental data. A clear thesis distinguishes a theory-level quantity from what the detector records and from what the reconstruction algorithm estimates. If a physics conclusion depends on detector acceptance, efficiency or reconstruction, describe that link rather than jumping directly from a histogram to a theory statement.

36. Treat detector-data analysis as a pipeline

FY30A1100 From Pulse Shapes to Physics follows the route from raw detector signals through noise reduction, calibration, signal extraction, reconstruction, statistical testing and physics interpretation. Document each material transformation. If thresholding, filtering, calibration constants or event-selection rules change, show how the decision was made. A final physics distribution cannot be evaluated properly if the steps converting raw signals into that distribution are hidden.

37. Carry uncertainty through to the final physics result

FY30A1100 explicitly includes uncertainty quantification and statistical methods. Identify the important statistical and systematic uncertainty sources and explain how they affect the final quantity. Do not report many decimal places simply because software produces them. If one calibration or model assumption dominates the uncertainty, make that visible in the discussion and avoid conclusions stronger than the uncertainty allows.

38. Keep real data and simulated data traceable

Particle, detector and electronics projects often combine simulation with measurements. Label which dataset is real, simulated or derived. Record generator/model configuration, detector simulation, calibration version and event selection when relevant. If data and simulation disagree, do not silently tune until they match. Explain what changed and why, and keep a versioned record of the configuration used for the thesis figures.

39. Reproducibility needs code, data versions and environment information

For computational work, preserve the code revision, material library/package versions, configuration files, random seeds where relevant, and the exact data or simulation snapshot used for the final result. Figures and tables should be regenerable from the preserved workflow rather than edited manually as independent artifacts. If raw company or research data cannot be shared publicly, reproducibility can still be maintained internally under the agreed access controls.

40. Literature comparisons need comparable quantities

Before claiming that your device, material, detector or model performs better than published work, check that the compared quantities have the same definition, units, boundary conditions and measurement context. Resolution at one energy is not automatically comparable with resolution at another. A lifetime under one radiation field is not directly a lifetime under another. State the comparison boundary so the reader can see what is genuinely comparable.

41. Ethics review depends on the actual study design

A normal simulation, materials or detector study does not automatically require human-sciences ethical review. However, if an Applied Physics project adds human participants, behavioural testing, interviews, sensitive personal data or another design that falls under ethical-review criteria, the route changes. LUT advises designing basic-degree theses so formal review is avoided where appropriate, but if review is required the student must apply together with the supervisor before data collection.

42. Personal data changes both data handling and AI use

If a technical project contains identifiable participant data, images, location information or other personal data, plan access, minimisation, retention and disclosure appropriately. Current LUT guidance says personal data or research material containing personal information must not be entered into AI applications. Participants must also be informed where AI is used in data analysis or to process their personal data/research material according to the applicable guidance.

43. Use the thesis seminar to test the evidence chain, not only presentation style

Before the seminar, prepare a concise chain: research question, method, key assumptions, main result, uncertainty/limitations and conclusion. Ask whether each conclusion is supported by the evidence shown. A seminar question that exposes an unsupported generalisation is useful because it can be fixed before examination. Record substantial feedback and decide explicitly whether it changes the analysis, discussion or scope of the final claim.

44. Perform one full reproduction check before examination

Choose one headline figure, table or numerical result and regenerate it from the documented starting data or simulation configuration. Check units, sample selection, calibration, code version, parameter file, random seed policy and post-processing. If the reproduced value differs materially from the thesis, investigate the pipeline rather than manually changing the number. This is especially important when the analysis has passed through multiple notebooks, instruments or software tools.

45. Final Applied Physics checklist

Before formal submission, confirm FyDAPhy_KJ 2026-2027, the 120 ECTS degree, FY30A1200 Master’s Thesis and Seminar, 30 ECTS, 0-5 grading with thesis 100%, the embedded seminar requirement and LUTKYPSYT 0 ECTS. Confirm topic approval, both supervisors, examiner route and your individual maturity-test implementation. Then check research-plan alignment, calibration/simulation assumptions, uncertainty, reproducibility, ethics/data protection where relevant, confidentiality, AI use, Turnitin, Form 1A/Form 1B, HOTT, Dean approval, Sisu transfer and LUTPub publication against the live LUT instructions.