Προκήρυξη θέσης Υποψήφιου Διδάκτορα στο Πανεπιστήμιο Θεσσαλίας
Προκήρυξη
Προκηρύσσεται μία θέση Υποψήφιου Διδάκτορα στο Πανεπιστήμιο Θεσσαλίας, στο Τμήμα Δημόσιας και Ενιαίας Υγείας, με θέμα διδακτορικής διατριβής:
«Ανάδειξη και αξιοποίηση μοναδικών πεπτιδίων στο πλαίσιο της Ενιαίας Υγείας: εφαρμογές στην ασφάλεια τροφίμων και την ανίχνευση νοθείας στα τρόφιμα».
Το αντικείμενο της διατριβής γεφυρώνει τη βιοπληροφορική με τη βιολογία και την ιατρική στο πλαίσιο της προσέγγισης One Health/Ενιαία Υγεία και απαιτεί ουσιαστική διεπιστημονική κατάρτιση.
Οι υποψήφιοι θα πρέπει να διαθέτουν υπόβαθρο στη Βιολογία, ή τη Χημεία ή τις Επιστήμες Υγείας ή την Πληροφορική και τους Αλγόριθμους και να έχουν ισχυρό ενδιαφέρον για συνεργασία μεταξύ των διαφορετικών επιστημονικών πεδίων.
Υποψήφιοι με υπόβαθρο στη βιολογία ή στις επιστήμες υγείας αναμένεται να αναπτύξουν δεξιότητες σε αλγοριθμική σκέψη και υπολογιστικές μεθόδους, ενώ υποψήφιοι από τον χώρο της Πληροφορικής θα εξοικειωθούν σε βάθος με βασικές αρχές βιολογίας και ιατρικής.
Προσφέρεται υποτροφία ύψους 6.000€ ετησίως για τρία έτη, με την προϋπόθεση κάθε έτος να υπάρξει είτε μια δημοσίευση σε περιοδικό Q1-Q2 είτε μία παρουσίαση σε συνέδριο.
Οι ενδιαφερόμενοι μπορούν να στείλουν τα βιογραφικά τους στον Δρ. Τσάγκαρη Γεώργιο (gthtsangaris@bioacademy.gr) και στην Αναπλ. Καθ. Κατσαφάδου Αγγελική (agkatsaf@uth.gr) μέχρι τις 24 Απριλίου 2026.
Δηλώσεις μεταπτυχιακών μαθημάτων και Διπλωματικών Εργασιών Εαρινού εξαμήνου 2025-26
Καλούνται οι μεταπτυχιακοί φοιτητές ΜΕ ΕΤΗ ΕΙΣΑΓΩΓΗΣ 2018, 2019 ΚΑΙ 2020 των ΠΜΣ και ΔΠΜΣ:
- Πληροφορική
- Μηχανική Υπολογιστών, Τηλεπικοινωνιών και Δικτύων
- Τεχνολογίες Πληροφορικής και Επικοινωνιών
- Γλωσσική Τεχνολογία
- Αλγόριθμοι, Λογική και Διακριτά Μαθηματικά
- Data Science and Information Technologies
- Διαστημικές Τεχνολογίες, Εφαρμογές και Υπηρεσίες
- Διοίκηση και Οικονομική των Τηλεπικοινωνιακών Δικτύων και Πληροφοριακών Συστημάτων
να δηλώσουν έως 6 μαθήματα εαρινού εξαμήνου ή/και Διπλωματική Εργασία, από τις 26.03.2026 ως τις 26.04.2026 στο Πληροφοριακό Σύστημα Διαχείρισης Μεταπτυχιακών Σπουδών:
https://pgs.di.uoa.gr
- Καλούνται οι μεταπτυχιακοί φοιτητές ΜΕ ΕΤΗ ΕΙΣΑΓΩΓΗΣ 2021, 2022, 2023, 2024 και 2025 όλων των παραπάνω ΠΜΣ και ΔΠΜΣ να δηλώσουν έως 6 μαθήματα εαρινού εξαμήνου ή/και Διπλωματική Εργασία, από τις από τις 26.03.2026 ως τις 26.04.2026 στην Πύλη Φοιτητολογίου του ΕΚΠΑ:
https://my-uni.uoa.gr/
- Η δήλωση μαθημάτων είναι υποχρεωτική για όλους τους μεταπτυχιακούς φοιτητές ΜΕ ΕΤΗ ΕΙΣΑΓΩΓΗΣ 2018, 2019, 2020, 2021 , 2022 , 2023, 2024 ΚΑΙ 2025.
2025-26 (Ε) ΑΝΑΚΟΙΝΩΣΗ ΔΗΛΩΣΕΩΝ ΠΜΣ 2025-26 (Ε) ΑΝΑΚΟΙΝΩΣΗ ΔΗΛΩΣΕΩΝ ΠΜΣ
MSc Thesis presentation of Mr. Alexios Kostakis – Monday, 30/3/2026
Program: “Data Science and Information Technologies”
Title: “Study of spatiotemporal changes within the abandoned flotation plant site of Kirki (NE Greece) via clustering on Sentinel-2”
The abandoned flotation plant at Kirki, northeastern Greece, represents one of the most prominent unmanaged mining legacies in the eastern Mediterranean. Since operations ceased in 1997, seven tailing ponds have remained exposed to atmospheric weathering, generating persistent acid mine drainage and mobilizing heavy metals into the surrounding drainage network. This thesis applies spatio-temporal remote sensing to characterize contamination patterns across the Kirki tailings complex between 2015 and 2022. Multiple cloud-free Sentinel-2 acquisitions were processed to extract per-pixel reflectance time series from 458 active pixels distributed across seven tailing ponds and three near-pond zones, comparing each pixel’s spectral signature against a library of mineral endmembers using Pearson correlation. The temporal evolution of mineral assemblages was analyzed through K-means clustering, hierarchical clustering with Ward’s linkage, and the Hungarian algorithm for cross-date cluster matching. Four geochemically distinct clusters emerge consistently across the observation period, mapping to discrete mineralogical regimes ranging from dynamic butlerite-dominated transition zones to stable jarosite-dominated cores. A central finding is that spectral contamination signatures do not respect physical pond boundaries, with near-pond peripheral regions expressing contamination footprints comparable to or greater than any individual pond. Precipitation emerges as the dominant environmental control, with cumulative rainfall triggering anomalous mineral dissolution and cluster reorganization across the site.
Examination Committee:
Assoc. Prof. Aggelos Pikrakis
Join Zoom Meeting
Meeting ID: 853 2495 8082
Passcode: 734838
MSc Thesis presentation of Mr. Nektarios Christou Monday, March 2, 2026
Student: “Nektarios I. Christou”
Program: “Data Science and Information Technologies”
Title: “A Spatial Attention Mechanism for Motion Prediction in Autonomous
Driving”
Abstract:
Accurate and efficient motion forecasting is essential for safe autonomous
driving, where an ego vehicle must anticipate the future trajectories of
surrounding agents in complex urban environments. Recent transformer based
architectures such as HPTR provide a strong and lightweight backbone for
trajectory prediction by jointly encoding map and agent information. In
this thesis we build directly on HPTR and study how to augment its
attention mechanism with an explicit spatial density prior. Our main
contribution is a Spatial Density Module (SDM) that models the interaction
space with a Gaussian Mixture Model and uses its normalized density as a
soft, spatially aware filter on attention maps. This gives a Spatial
Density Transformer (SDT) variant that keeps HPTR’s encoder-decoder
design, number of parameters, and training setup, and changes only the way
attention is distributed over possible interaction regions. We conduct
experiments on the Waymo Open Motion Dataset and focus the analysis on the
behavior of the Spatial Density Module. In an ablation study over the
number of mixture components and SDM hyperparameters, and using simple
diagnostic metrics for the mixtures (component utilization, spatial
variance, and entropy), we
observe that the learned density tends to place mass in regions where
there are agents and map elements instead of empty areas. This suggests
that the SDM does learn a meaningful notion of interaction density. In
terms of standard forecasting metrics on WOMD, SDT reaches reasonable
performance, but we do not carry out a full head to head comparison with
HPTR under identical conditions, so we cannot draw firm conclusions about
relative gains or losses. The main contribution of this work is therefore
the design and analysis of the density aware attention mechanism and its
diagnostics, rather than a clear improvement in benchmark scores. The
experiments indicate that adding a learned, density based attention filter
on top of an existing lightweight transformer is a viable direction that
could be explored further in future work, including variants where the
mixtures are constrained to behave like k nearest neighbor neighborhoods
as in HPTR to better understand how useful density aware attention is in
practice.”
Date/Time: March 2, 2026 – 4:00 PM.
Examination Comitee:
”
Assoc. Prof. Aggelos Pikrakis
Prof. Dimitrios Gunopulos
Dr. Konstantinos Koutroumbas
“
Presentation link:
https://unipi.webex.com/unipi/j.php?MTID=m8f41d115c98f3ec6aa42bcfedafc0041
MSc Thesis presentation of Mr. Nikolaos Charisis, Monday, March 2, 2026
Student: “Nikolaos Charisis”
Program: “Data Science and Information Technologies”
Title: “A Comparative Analysis of Classical Machine Learning Methods and
Variational Quantum Methods for Breast Tumor Classification”
Abstract:
“Breast cancer accounts for 36% of female oncological cases, necessitating
high-precision prognostic tools. While traditional machine learning is the
standard, this study explores the efficacy of Variational Quantum
Classifiers (VQCs) using breast tumor data from fine-needle aspirates. By
integrating Principal Component Analysis (PCA), we managed the tradeoff
between explained variance and the dimensions of the Hilbert space
traversed by the quantum processor. The findings reveal that a 4-qubit
configuration utilizing 16 principal components captured nearly 99% of
data variance, consistently achieving classification scores above 90%.
This performance demonstrates that even small-scale quantum circuits can
remain competitive with sophisticated classical methods like Logistic
Regression. Ultimately, the study highlights how hybrid quantum-classical
pipelines with a small number of qubits can leverage quantum advantages to
provide robust, scalable solutions for complex medical diagnostics.”
Date and time of presentation: Monday, March 2, 5.00 p.m.
Examination Comitee:
”
Dr. Aikaterini Mandilara
Prof. Dimitrios Gunopulos
Prof. Dimitrios Syvridis
”
Meeting Link:
https://meet.google.com/ukg-bowy-ejb
MSc Thesis presentation of Mr. Ioannis Mystakidis, Friday, February 27, 2026
On Friday, February 27, 2026, at 17:00 pm, Mr. Ioannis Mystakidis, student of the postgraduate program “Data Science and Information Technologies”, will present his MSc thesis titled: “Integration of functional enrichment analysis techniques” Title: “Integration of functional enrichment analysis techniques” Ioannis Mystakidis, DSIT MSc Abstract Functional enrichment analysis is a standard step in interpreting omics experiments, yet its results are fragile because they depend on choices that are often implicit: tool selection, background definition, knowledge-base selection, statistical test, multiple-testing correction, and filtering thresholds. The central issue is not a lack of software but a lack of workflow support that makes these choices explicit and comparable. A literature synthesis identifies recurring pitfalls and translates them into design requirements. Based on these requirements, comparison axes are defined to capture how enrichment runs legitimately differ across tools, knowledge bases, parameters, and analysis settings. FLAME v3.0 implements this comparison-first workflow as a web platform that sits above the fragmented enrichment tool landscape rather than replacing it. The platform integrates six enrichment tools for over-representation analysis through direct connectors, where execution is controlled and provenance is captured automatically. All results are normalised into a canonical schema that decouples the comparison engine from the tools that produced them. A dedicated comparison view combines results across runs using Fisher’s combined probability test and effective agreement counts, and presents them through complementary visualisations that reveal where tools agree, where they diverge, and which findings depend on a single tool’s behaviour. The modular architecture is designed so that additional tools and enrichment paradigms can be integrated without changes to the comparison layer. Examiners Prof. Dimitrios Stravopodis, Prof. Ourania Tsitsilonis, Dr. Georgios Pavlopoulos link https://meet.google.com/gbg-mvby-dfn