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Otakaari 1 grandhall. Photo: Esa Kapila
 

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Recent Submissions

Variational quantum circuits for a charged Higgs boson search in CMS proton-proton collisions at 13.6 TeV
(2026-02-14) Kössi, Aapo
School of Science | Master's thesis
Quantum machine learning (QML) has demonstrated exponential advantages in some specific, suitable learning tasks. For practical applications, these benefits remain difficult to verify. This thesis studies the applicability of QML in the context of a search for a charged Higgs boson decaying to a tau lepton and a tau neutrino in the fully hadronic channel. Classical deep neural networks act as a baseline against implemented quantum models. Expected exclusion limits for the charged Higgs boson with this decay channel are estimated using a traditional set of selection criteria, and using the machine learning classifiers. The limits are found to be compatible with previous results. Classical machine learning is found to outperform the implemented variational quantum circuits in inference, but this gap does not translate to an advantage in the statistical analysis sensitivity to the charged Higgs boson production rate. The statistical results identify two regions, where machine learning classification is likely to only benefit in analysis of the lighter considered masses. This work presents the first look into the expected sensitivity of this channel using 18.7 inverse femtobarns of collision data recorded in 2023 by the CMS detector at a center-of-mass energy of 13.6 TeV.
Towards integrating artificial intelligence – Assisted documentation into clinical workflows
(2026-01-19) Klemetti, Louna
School of Science | Master's thesis
Clinical documentation is a central yet increasingly burdening part of physicians’ work. Electronic health records (EHRs) have expanded documentation requirements beyond clinical reasoning to include legal, administrative, and organisational purposes, which has contributed to information overload, fragmented workflows, and reduced time available for patient care. In response, artificial intelligence (AI)-based documentation tools are being promoted as a way to reduce the documentation burden and support clinical work. However, there is still limited qualitative understanding of how physicians themselves experience current documentation practices, what they expect from AI-assisted documentation, and under what conditions such tools are perceived useful, trustworthy, and professionally acceptable. This thesis examines Finnish physicians’ experiences of their current documentation workflows and their anticipatory evaluations of AI-assisted documentation. The study employs a qualitative, user-centered, and socio-technical approach that draws on semi-structured interviews conducted with seven physicians working in different specialised care contexts. The results show that physicians experience documentation as a multi-stage workflow, where the primary burden is not on writing the encounter notes but on navigating, reviewing, and synthesising fragmented and redundant patient information. AI-assisted documentation is widely envisioned as a supporting background technology rather than a workflow driver. The strongest expectations focused on intelligent information synthesis, context-aware search, and automation of repetitive action-oriented tasks. Anticipated risks centre on verification effort, loss of narrative control, and professional responsibility. Trust is found to be conditional and task-specific. It is shaped by perceived reliability, workflow fit, and respect for professional autonomy rather than abstract performance claims. By situating anticipated user experience (UX) within physicians’ lived practices, this thesis contributes empirical insight into how AI-assisted documentation is evaluated at an early phase of adoption in the Finnish healthcare context and cautions against solutions that may replicate or reinforce existing challenges.
Determinism and probability
(2026-03-11) Strand, Anton
School of Science | Bachelor's thesis
Probability is an important concept used in many different fields. Despite this, there are multiple interpretations of what probability means. According to classical logic, a proposition is either true or false, and its truth value is constant. This means that true propositions have always been true, and if a proposition is not true, it will always be false. If these presuppositions are accepted, it is natural to affirm the theory that reality is as it is, and could not have been otherwise. This is called determinism, a theory which implies that even phenomena that are often considered random, such as nuclear decay, are in fact predetermined down to the smallest detail. Determinism is a theory that impacts how probability is understood, since probability is often closely tied to randomness. The aim of this thesis is to present an interpretation of probability that is consistent with determinism. A good interpretation should follow some mathematical axiomatization of probability. After Kolmogorov laid out his axiomatization it has been common to use it. Countable additivity is often used as an axiom of probability, but the advantage of choosing finite additivity as an axiom instead is that this makes it possible to assign a uniform probability distribution over a countably infinite sample space, which is not possible with countable additivity. There are multiple interpretations of probability in the literature. Some of them are consistent with Kolmogorov’s axiomatization, and some are based on some other axiomatization. In addition to being consistent with a mathematical axiomatization, a good interpretation of probability should also align with how people generally use the term “probability”. The problem with many interpretations is that there are examples which demonstrate that they do not align with how the term “probability” is used. After assessing the validity of different interpretations of probability, their problems and strengths as well as how consistent they are with determinism, it is suggested in this thesis that probability should be understood as the objective degree of warrant for believing a proposition. This interpretation follows Kolmogorov’s axiomatization with the axiom of finite additivity, and it is consistent with both determinism and how the term “probability” is used. If a stochastic variable is used multiple times with the same probability distribution to map to a number, then the value you have the most warrant to believe to be the mean value converges toward the expected value. This is the interpretation of expected value according to the interpretation of probability presented in this thesis. This interpretation of probability is very similar to a so-called evidential interpretation, where probability is understood as the degree to which the given evidence supports a hypothesis. Depending on how the language of the evidential interpretation is understood, these two interpretations might be different ways of speaking about the same interpretation.
Machine learning applications in enhancing sustainable supply chains—a foresighted empirical study
(2026) Farshadfar, Zeinab
School of Science | Doctoral thesis (article-based) | Defence date: 2026-03-20
This dissertation explores the role of machine learning (ML) in advancing sustainable and circular supply chains (CSCs). By drawing on the sustainable supply chain management (SSCM) field and applying the triple bottom line (TBL) as a classification lens, the doctoral dissertation identifies gaps in current research. It is worth noting that this doctoral dissertation does not aim to develop a new theory in SSCM; rather, it identifies and addresses specific gaps within the existing SSCM literature through empirical and methodological contributions. Although literature on ML in supply chain management (SCM) has expanded rapidly, most research remains conceptual or simulation-based, providing limited empirical evidence of real-world impact. This study addresses that gap by combining a systematic literature review (SLR) with both real-world and simulation-based pioneer case studies to assess how ML contributes to the sustainability aspects of supply chains (SCs). Two main shortcomings emerge from previous research. First, ML is often used at the decision-support level—predicting demand, ranking suppliers, or creating optimisation inputs—where its sustainability impact relies on managerial action rather than operational integration. Second, even when operational ML is examined, its effects on sustainability outcomes are rarely measured directly. Instead, improvements are credited to broader optimisation frameworks, leaving ML’s unique role unclear. These limitations matter because, without real-world deployment and cost–benefit analysis, ML’s transformative potential for circularity remains largely aspirational. This dissertation adopts a foresighted empirical approach, concentrating on three SC functions with high sustainability potential: construction waste management, aviation spare parts (SPs) provisioning, and CSC optimisation. The pioneering case studies demonstrate that ML-enabled waste sorting surpasses traditional methods by increasing recycling rates and lowering long-term costs in high-wage environments. Furthermore, the sensitivity analyses on the same ML-enabled waste sorting case emphasise that labour costs, discount rates, and equipment investments are critical factors for the competitive advantage of ML-enabled waste sorting. In aviation, SP inventory pooling, ML-enabled generative design (GD), and additive manufacturing reduce CO₂ emissions through lightweighting and determine the cost and durability thresholds necessary for the economic viability of ML-enabled GD. Beyond the mentioned insights, the dissertation presents a structured synthesis of ML applications across SC, highlighting the predominance of ML supervised learning methods and the limited empirical evidence regarding their operational integration. By evaluating the impacts of ML using cost models and sensitivity analyses, this doctoral research provides a more accurate quantification of ML’s role in advancing SC sustainability. Overall, this doctoral research bridges the gap between conceptual enthusiasm and empirical evidence, demonstrating that ML-enabled solutions can serve not only as a decision-support tool but also as an operational technology that reduces costs, minimises waste (based on article 2 findings), extends product lifecycles (based on article 4 findings), and supports circular flows (based on article 1 findings). Simultaneously, this doctoral research identifies the enabling conditions—economic, technological, and policy-related—that influence adoption, providing a foresighted perspective for both scholars and practitioners on the future of sustainable and CSCs.
Entry strategies to ecosystemic technology markets
(2026-02-15) Turkkila, Aleksi
School of Science | Master's thesis
Developing proprietary technologies that can become industry standards offers companies significant competitive and financial advantages. However, achieving widespread adoption in complex, ecosystem-driven markets is a major challenge. This thesis investigates how companies introduce and scale proprietary technologies within ecosystemic technology markets characterized by interdependent actors and value co-creation. The study aims to identify effective entry strategies and the ecosystem dynamics that firms leverage during this process. Using a multiple case study methodology based on secondary data from publicly available sources, the research examines six technology companies operating in various roles and stages of market entry. The analysis reveals a new model of technology introduction centered around integrator-driven ecosystems, where a key actor assembles value for end users by combining complementarities. Additional strategies identified include the use of technology branding, direct involvement in producing complementary products, and participation in formal standardization processes. This thesis contributes to the literature by integrating ecosystem theory with technology market entry strategy. It provides practical insights for firms seeking to position their technologies as de facto or formal standards in ecosystemic environments.