igartua / gral-tfg Bachelor's theses in Physics · Josu M. Igartua · EHU igartua / gral-tfg Trabajos de Fin de Grado en Física · Josu M. Igartua · EHU igartua / gral-tfg Fisikako Gradu Amaierako Lanak · Josu M. Igartua · EHU

Proposals

Proposals

Topics I am prepared to direct in the 2026–2027 course. Each page states the physics, the methods, the starting material and what a finished thesis would contain. A topic of your own is welcome if it meets the same standard: write to josu.igartua@ehu.eus.

2026–20275 proposals
Reproduce the career-trajectory analysis of gender inequality in science from Huang et al. (2020) on open bibliometric data, then narrow it to physics graduates of the UPV/EHU and turn the results into local role models.
Women in physics / Tracing paths and breaking walls / Inspiring the new
Science of scienceData analysisGender and physicsSurvival analysis
Basis paper at PNAS
OpenAlex (open bibliographic data)
Reproduce the scaling analysis of gene and protein lengths in Muro et al. (2025) from public genome data, simulate the multiplicative growth model behind it, and test how robust the proposed critical point is.
Life's great leap forward / Complexity hits a wall / Phase transition solves
Statistical physicsPhase transitionsBiophysicsData analysis
Basis paper (PDF, CC BY)
Supporting information (PDF)
Article at PNAS
Reproduce the rigid-body simulation of a bowling ball on an oiled lane from Ji et al. (2025), then use it to ask what the paper does not: other oil patterns, other balls, a fuller model of the bowler.
Rolling down the lane / Physics guides the striking ball / Perfect game awaits
MechanicsRigid-body dynamicsNumerical simulationSports physics
Basis paper (PDF, CC BY)
Article at AIP Advances
Reproduce the prestige-hierarchy analysis of Clauset, Arbesman and Larremore (2015) on their open faculty-hiring data, then build an agent-based model of a numerus-clausus degree whose prestige feeds back on who applies.
Prestige drives the crowd / Vocation fades in the noise / Model finds the truth
Social physicsComplex networksAgent-based modellingPython
Basis paper at Science Advances (open access)
Second reading, Matthew effect (PNAS)
Reproduce the bounded-confidence opinion model of Hegselmann and Krause (2002), recover its consensus, polarisation and fragmentation regimes, then put the model on real network topologies and treat the transitions as a statistical physicist would.
Opinions diverge / Networks echo and divide / Physics models why
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