These are starting points, not funded positions. Each can be scoped down to a graduation project or up to a doctoral line, and the final question is one we shape together. All six come back to the central question of my research agenda: how do we make better decisions when signals are incomplete, noisy and expensive?
01 Thread 1 · Sensor-suite design
Multi-objective sensor selection and placement
Which sensors should a system carry, and where? Treat the choice as a search over competing objectives such as cost, diagnostic coverage and reliability, using evolutionary algorithms like NSGA-II/III or MOEA/D. Cases can come from aircraft subsystems, industrial plant or power-distribution networks.
NSGA-II · NSGA-III · MOEA/D · Pareto fronts
02 Thread 1 · Sensor-suite design
Diagnostic-contribution scoring on open fault datasets
NDCI scores each sensor by how well it separates faults, how sensitive it is to fault severity, and what it adds that no other sensor does. Take it beyond the thesis models onto open fault datasets: where does the ranking hold, and where does it break, for example when degradation shifts over time?
NDCI · sensor ranking · mRMR baseline
03 Thread 3 · Low-cost sensing
Acoustic anomaly detection with low-cost microphones
A commodity microphone can carry a surprising amount of diagnostic information. Build and test methods that flag abnormal sound from rotating machines such as motors and pumps, and measure honestly how far a cheap sensor can go before it needs help.
spectral features · anomaly detection · baselines
04 Thread 2 · Honest uncertainty
Calibrated uncertainty for fault diagnosis and prognostics
A diagnosis that returns a short set of plausible faults with a stated coverage is often more useful than one confident label. Use conformal prediction and related methods to attach calibrated uncertainty to fault classifiers and remaining-useful-life estimates.
conformal prediction · PHM · remaining useful life
05 Thread 3 · Efficient AI
Small language models on CPU, with Turkish tasks
Which open-weight language model is worth running on an ordinary laptop with no GPU, and how good is it in Turkish? Measure speed, memory and answer quality on a reproducible benchmark, and make every result something another person can verify.
benchmarking · quantised models · Turkish evaluation
06 Method · all threads
Multi-objective optimisation benchmarks and visualisation
Compare multi-objective algorithms fairly on standard test problems with quality indicators such as hypervolume, and work on making Pareto fronts readable for the people who have to choose one point on them.
test suites · hypervolume · decision support
Your own idea is welcome if it connects to one of the three threads. Send a paragraph about it with your enquiry.