AI Research
Applied AI Scientist
Applied AI scientists live in the productive tension between research papers and product roadmaps. The work is reproducing a result from arxiv on a Tuesday, then deciding by Thursday whether it can be adapted to a problem nobody else has framed yet.
Days mix ablation studies, careful evaluation design, and conversations with engineers about what's realistic to ship. Good work here looks like an experiment that disproves your favorite hypothesis cleanly, then suggests a better one.
Students grow into this role by treating PyTorch and Hugging Face Transformers as their lab bench and learning to write up findings the way a scientist would — with assumptions, limitations, and a path for the next person to extend the work.
- AnalysisIntermediateNew
Optimize Stop-Loss Policies with Dynamic Programming at a Quant Fund
You receive five years of daily PnL series for 12 momentum strategies plus a small set of state features (rolling vol, drawdown, regime indicator). Calibrate a discrete Markov m…
- Dynamic Programming
- Backward Induction
- State Modeling
Decision Making Under Uncertainty - AnalysisBeginnerNew
Build a Restoration Workflow for a Digital Heritage Archive
You receive 50 high-resolution scans of glass plates plus 3 reference 'gold' restorations done by a senior conservator. Design a reproducible workflow combining inpainting for s…
- Image Restoration
- Inpainting
- Tone Mapping
Image Processing and Computational Imaging - AnalysisIntermediateNew
Simulate Hospital Bed Allocation for a Healthtech Decision Support Pilot
You receive 12 months of anonymized admissions and discharges data plus ward layouts (medicine, surgery, ICU, geriatrics) and a small set of clinical transfer rules. Build a dis…
- Discrete Event Simulation
- Simpy
- Policy Comparison
Decision Support Systems and Decision Analysis - CodeIntermediateNew
Build an Ensemble Strategy for Marketing-Mix Modelling
You receive 36 months of weekly marketing-spend and outcome data for 8 sample brands. Build a per-brand baseline gradient-boosting MMM model, then build two more base learners (…
- Ensemble Methods
- Stacking
- Time Series Cv
Machine Learning Practice your coursework on real scenarios.
Every challenge is shaped from real-world context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- ResearchBeginnerNew
Run a Human-Preference Study Comparing Two Coding Assistants
Design a blinded paired-comparison study: 12 developer participants, each gets the same 8 realistic coding tasks (refactor, write a function, debug, test), each task is solved b…
- Experimental Design
- Statistical Evaluation
- Human Evaluation
AI Measurement and Evaluation - ResearchIntermediateNew
Hardware-Aware NAS for a Wearable ECG Classifier
You receive a labeled subset of an arrhythmia ECG dataset (about 80,000 10-second windows, 4 classes), a microcontroller latency lookup table (op-level milliseconds) for a Corte…
- Neural Architecture Search
- Hardware Aware Design
- Edge Inference
Edge ML and On-Device Machine Learning - CodeIntermediateNew
Solve a Vehicle-Routing Problem with Tabu Search
You receive a week of anonymized daily VRPTW instances (around 800 orders per day, 120 vehicles, hard delivery windows). Implement tabu search with: a route-insertion constructi…
- Tabu Search
- Metaheuristics
- Vehicle Routing
Evolutionary Computation and Metaheuristic Search - AnalysisIntermediateNew
Audit BLEU vs. COMET on a Multilingual Customer-Support Corpus
You receive 600 source-translation-reference triples covering 6 languages (EN as source; ES/FR/DE/JA/PT-BR/HI as targets), each scored on adequacy and fluency (1-6) by 3 profess…
- Mt Evaluation
- Neural Mt
- Statistical Analysis
Machine Translation - Browse challenges
Explore role
Product Manager
Ship product that solves real user problems. Combine user research, prototyping, and stakeholder alignment to turn ambiguous briefs into measurable wins — the role at the centre of modern software teams.
- CodeIntermediateNew
Forecast Energy Demand for a Nordic Renewable Utility
You receive 5 years of hourly residential-segment demand, hourly weather data (temperature, wind, irradiance), and a calendar of public holidays. Build a probabilistic forecaste…
- Time Series Forecasting
- Probabilistic Modeling
- Feature Engineering
Applied Machine Learning - ResearchIntermediateNew
Fine-Tune a Vision-Language Model for Image Captioning
Take BLIP-2 or LLaVA-1.6 as the base. Fine-tune (LoRA is fine) on a 4,000-image accessibility-curated dataset where each image has a useful caption written by a low-vision-exper…
- Vision Language Models
- Fine Tuning
- Pytorch Or Tensorflow
Multimodal Machine Learning - AnalysisBeginnerNew
Cost-Optimize an Embedding Pipeline for a Customer Support Knowledge Base
You receive: (a) the current pipeline (full re-embed on any article change, OpenAI text-embedding-3-large, 3,072 dims) with one month of cost logs, (b) a sample of 5,000 article…
- Embedding Models
- Finops & Cost Optimization
- Change Detection
Vector Databases and Embeddings - CodeBeginnerNew
Markov Random Field for Image Segmentation in Crop Monitoring
You receive 60 Sentinel-2 image tiles (10-meter resolution) over 12 vineyards, each tile with per-pixel disease labels from agronomist field walks. Take the consultancy's existi…
- Markov Random Fields
- Graph Cuts
- Image Segmentation
Probabilistic Graphical Models Build a verifiable portfolio.
Submissions become evidence. Reviewers with shipping experience score against a rubric; the result becomes a credential anyone can verify.
Why Ewance
- CodeIntermediateNew
Forecast Intraday FX Volatility for a London Liquidity Desk
You receive 18 months of tick-level mid-quote data for six FX pairs plus a calendar of scheduled macro events. Resample to 1-minute bars, engineer realized-volatility features, …
- Time Series Forecasting
- Feature Engineering
- Model Validation
AI and Quantitative Finance - AnalysisBeginnerNew
Evaluate Speech-to-Text Quality for a Contact-Center Analytics Vendor
You receive 200 anonymized call-recording snippets (2-4 minutes each, ~67 per language) with reference transcripts plus a domain glossary of about 600 product terms. Run all thr…
- Speech Recognition
- Sequence Models
- Model Evaluation
Machine Perception - CodeIntermediateNew
Build a Domain Instruction-Tuning Recipe for a Legal Coach
You will source instruction data from three streams: ~3,000 synthetic paralegal Q&A generated by a frontier model (anonymized prompts), ~1,500 curated examples from public legal…
- Instruction Tuning
- Fine Tuning
- Data Curation
Large Language Models - CodeIntermediateNew
Plan Inventory Replenishment as an MDP for an E-Commerce AI Startup
You receive 18 months of daily demand for 50 representative SKUs at one warehouse plus lead-time and unit-cost data. For one SKU at a time, formulate an MDP with state = (on-han…
- Mdp Modeling
- Value Iteration
- Dynamic Programming
Decision Making Under Uncertainty - CodeBeginnerNew
Compare MDP Solvers for a Smart-Grid Battery Dispatch Pilot
Model home-battery dispatch as a finite MDP: state is (state-of-charge, hour-of-day, current price tier), actions are charge/hold/discharge with realistic efficiency losses, tra…
- Markov Decision Processes
- Value Iteration
- Policy Iteration
Artificial Intelligence: Principles and Techniques - AnalysisIntermediateNew
Frame an Energy-Storage Dispatch Decision as a Bayesian Decision Problem
You receive 2 years of hourly spot-price data, 2 years of wind generation data, and a manufacturer's battery degradation model. Frame dispatch as a Bayesian decision problem: mo…
- Bayesian Decision Theory
- Price Modeling
- Back Testing
Decision Making Under Uncertainty - ResearchBeginnerNew
Evaluate Speech Synthesis Voices for an EdTech Storyteller App
You will generate 60 audio clips (20 per vendor) covering 4 story genres and 3 emotional tones. Recruit 15 native Spanish speakers via a remote panel (Prolific or local equivale…
- Tts Evaluation
- Listening Studies
- Mos Scoring
Speech Recognition and Spoken Language Processing - ResearchIntermediateNew
Tune a PPO Policy for an Energy-Storage Trading Bot
You receive 18 months of 15-minute Nordic spot-price data, a battery dynamics model (capacity, round-trip efficiency, degradation curve), and a rule-based baseline that earns ab…
- Policy Gradients
- Ppo
- Reinforcement Learning
Deep Reinforcement Learning - ResearchBeginnerNew
Drug-Repurposing Candidate Screen with Embedding Similarity
You receive (1) a list of 15 known therapeutic candidates (SMILES + ChEMBL identifiers) for a single rare disease, (2) a database of about 4,500 marketed drugs (SMILES + ATC cod…
- Molecular Embeddings
- Similarity Search
- Transfer Learning
Machine Learning for Healthcare and Biomedicine - CodeBeginnerNew
Optimize Wind-Turbine Layout with a Genetic Algorithm
You receive a wind-speed-and-direction time series for the lease area, the polygon boundary, a minimum inter-turbine spacing constraint, and a Jensen wake model. Implement a rea…
- Genetic Algorithms
- Metaheuristics
- Constraint Handling
Evolutionary Computation and Metaheuristic Search - CodeSeniorNew
Offline RL for Robot-Arm Skill Reuse
You receive 5,000 logged trajectories (state, action, reward, next-state) across 12 tasks, with 9 tasks for training and 3 held out. Train an offline RL algorithm (CQL or IQL re…
- Offline Rl
- Conservative Q Learning
- Skill Reuse
Robot Learning - ResearchIntermediateNew
Disease-Progression Modelling for a Neurodegeneration Biotech
You receive a curated longitudinal Parkinson's cohort (about 1,200 patients, 4-12 visits each, MDS-UPDRS sub-scores, cognitive assessments, demographics). Fit (1) a linear mixed…
- Disease Progression Modeling
- Mixed Effects Models
- State Space Models
Machine Learning for Healthcare and Biomedicine - ResearchBeginnerNew
Evaluate Open-Source Embedding Models for a Multilingual Help Center
You receive 1,200 labeled (query, relevant-help-article) pairs across 6 languages plus the help-center corpus (~25,000 articles). Index the corpus with each of 4 open-source mul…
- Multilingual Embeddings
- Dense Retrieval
- Ir Evaluation
Information Retrieval and Search - ResearchBeginnerNew
Hyperparameter Search via CMA-ES for a Pharma QSAR Model
You receive a labeled QSAR dataset (around 25,000 compounds, regression on a binding-affinity target), a fixed feature pipeline (Morgan fingerprints + descriptors), and the team…
- Cma Es
- Metaheuristics
- Hyperparameter Optimization
Evolutionary Computation and Metaheuristic Search - CodeIntermediateNew
Build a 30-Day Readmission Risk Model on De-Identified EHR Data
You receive a curated MIMIC-style de-identified EHR cohort (about 28,000 admissions, demographics, comorbidities, labs, prior-admission counts) with 30-day readmission labels. T…
- Ehr Modeling
- Risk Stratification
- Model Calibration
Machine Learning for Healthcare and Biomedicine - CodeIntermediateNew
Build a Neural Surrogate for Computational Fluid Dynamics in HVAC Design
Use a published CFD dataset (e.g., AirfRANS or a small in-house dataset if available) of around 1,000 steady-state airflow simulations on 2D building zones. Train a Fourier Neur…
- Neural Operators
- Surrogate Modeling
- Computational Fluid Dynamics
AI for Science and Engineering
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
Related roles you may want to explore
Browse all roles →AI Research
AI Safety Researcher
Think of this role as the loyal opposition inside an AI lab. While teammates race to make a model more capable, AI safety researchers ask what happens when it succeeds — at the wrong thing, for the wrong reasons, in the wrong hands. The work spans red-teaming prompts, designing constitutional methods that nudge models toward principled behavior, and translating findings into guardrails that product teams can actually adopt. Good work here is rigorous and humble: it admits what's still unknown rather than papering over it. Students grow into this path by pairing technical depth in PyTorch with reading widely across ethics, policy, and security. The field rewards people who can hold both at once.
AI Research
ML Researcher
What if attention worked differently? What if a smaller model, trained better, could match a much larger one? ML researchers chase questions like these for a living. The role exists to push the frontier of what models can do — through careful ablation studies, novel architectures, and the patient grind of running experiments that often disprove your favorite hypothesis. Days mix reading recent papers, sketching ideas, and writing JAX or PyTorch code that someone else will read in six months. Students grow into this path through reproducing published results before inventing their own, and learning to write up findings with intellectual honesty. The best researchers stay curious about why something worked, not just that it did.
AI Research
Research Scientist
What does a model actually learn, and can we prove it? Research scientists in AI labs spend their careers refining that question. The work alternates between long stretches of reading, careful ablation studies in PyTorch, and the rare moment when a benchmark moves and you understand why. CUDA kernels and diffusion model architectures sit in the toolkit, but the real currency is taste: knowing which experiment is worth a week of compute and which is a distraction. Students who thrive here tend to come from machine learning, physics, or pure math, and they read papers the way novelists read novels. Expect a long apprenticeship reproducing others' results before your own ideas earn a place at a top venue.
Industry teams behind a decade of practitioner briefs
Hiring from this pool?
Sponsor a challenge and meet candidates through actual work.
Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.