Experimental design
If you like applying Experimental design, every challenge here gives you a chance to practice it on a real industry brief.
- ResearchSeniorNew
Trajectory Prediction Model for Urban Robotaxis
Use the Argoverse 2 motion-forecasting dataset (open access). Train an LSTM baseline + a transformer challenger (e.g., a small Wayformer or HiVT). Evaluate on minADE/minFDE (min…
- Trajectory Prediction
- Transformer Models
- Evaluation
Open coursework - ResearchSeniorNew
Train Cooperative Agents with Multi-Agent RL
Pick an open multi-agent environment (PettingZoo's MPE 'simple_spread', Overcooked-AI, or SMAC). Implement or wrap three methods: IPPO (independent PPO per agent), MAPPO (centra…
- Multi Agent Reinforcement Learning
- Ppo
- Pytorch
Open coursework - ResearchSeniorNew
Reproduce a Mechanistic Interpretability Result on a Small Transformer
Pick a published mechanistic-interpretability paper that operates on a small (under 1 billion parameter) open-source transformer (e.g., GPT-2 small, Pythia 70M). Set up the envi…
- Mechanistic Interpretability
- Transformer Internals
- Pytorch
AI Safety and Alignment - ResearchSeniorNew
Pre-Register and Run a Small Neural-Network Ablation Study
You will study how three architectural and regularization choices (depth: 2/4/8 hidden layers; activation: ReLU vs. GELU; weight decay: 0 / 1e-4 / 1e-3) affect a small MLP's tes…
- Neural Networks
- Regularization
- Experiment Design
Open coursework 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
- AnalysisBeginnerNew
Chunking Strategy Bake-Off for Financial Filings
You receive 40 anonymized 10-K filings and 100 labeled questions split into 50 narrative (e.g., 'what is the company's main risk factor?') and 50 numerical (e.g., 'what was oper…
- Document Chunking
- Semantic Chunking
- Layout Aware Chunking
Retrieval-Augmented Generation - ResearchSeniorNew
SAT-Based Planner for Smart-Grid Demand Response
Encode the dispatch problem (which customers to curtail by how much, respecting per-customer contractual caps and grid-cell totals) as a SAT or MaxSAT instance. Solve 50 histori…
- Sat Based Planning
- Constraint Encoding
- Benchmarking
Automated Planning - ResearchBeginnerNew
Evaluate a Generative AI Image Tool with a Within-Subjects Study
You will write a study protocol, recruit 20 participants (a Discord callout is fine), counterbalance the two conditions, and run 45-minute sessions over Zoom. Collect three meas…
- Experiment Design
- User Study
- Within Subjects Design
Human-Computer Interaction for AI Systems - ResearchSeniorNew
Open-Vocabulary Segmentation Benchmark for a Robotics R&D Lab
Use a curated 200-image household scene set (publicly-available HM3D renderings or COCO + a handful of household prompts). Benchmark 3 open-vocabulary segmentation models: SAM +…
- Open Vocabulary Segmentation
- Vision Language Models
- Benchmarking
Computer Vision - 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.
- ResearchIntermediateNew
Recommend a Color Scale for a Flood-Risk Map With Evidence
Working only from the provided materials, reach and defend a single recommendation. Read the decision file (decision-file) to understand the actors, the prior complaint, and the…
- Perceptual Study
- Color Scales
- Experimental Design
Open coursework - ResearchIntermediateNew
Hands-on Lab: Reproduce a Recent SOTA Vision Paper
Pick one of three pre-approved 2025 papers (offered by the supervisor) with a known reference codebase you may consult but not copy. Re-implement the model and training loop in …
- Pytorch
- Paper Reproduction
- Experiment Design
Open coursework - CodeIntermediateNew
Train a Deep Q-Network for Warehouse Robot Routing
You receive a Gymnasium-compatible warehouse simulator (50x50 grid, 8 dynamic obstacle pedestrians, 20 randomized pick locations) and a baseline A* planner script. Train a DQN a…
- Deep Q Learning
- Reinforcement Learning
- Pytorch
Open coursework - ResearchSeniorNew
Train a Small Diffusion Model for Synthetic Defect Generation
You receive 2,000 labeled defect images and 18,000 clean weld images. Train a small class-conditional latent diffusion model on the defect images (Hugging Face diffusers is fine…
- Generative Perception
- Diffusion Models
- Data Augmentation
Open coursework 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
- ResearchSeniorNew
Neuromarketing Audit for a Sustainable Fashion Retailer
Your team will design and simulate a neuromarketing experiment. First, propose two alternative store layouts (e.g., circular vs. grid) and two website homepage designs (e.g., he…
- Neuromarketing
- Experimental Design
- Eye Tracking Analysis
Consumer Behavior - DesignBeginnerNew
A/B-Test a Recommender Improvement Without Breaking Trust
You receive offline-evaluation results for both the production and candidate models plus aggregate metrics from the last 12 weeks (recipe views, save rate, weekly active users, …
- Experiment Design
- Ab Testing
- Metric Design
Machine Learning in Practice - ResearchSeniorNew
Benchmark Long-Context Architectures on a Legal-Doc Retrieval Task
You receive a public legal-QA dataset (e.g., LongBench's legal split or similar) filtered to documents over 50,000 tokens. Implement or wrap 3 architectures: a sliding-window Tr…
- Long Context Architectures
- State Space Models
- Transformers
Advanced Deep Learning - ResearchSeniorNew
Quantify Sim-to-Real Gap for a Warehouse Manipulation Policy
You receive a trained pick-and-place policy (PyTorch), the simulation env (Isaac Lab), and access to a real-arm rig (or recorded teleop episodes if hardware is unavailable). Def…
- Sim To Real
- Manipulation
- Experiment Design
Open coursework - ResearchIntermediateNew
Train a NeRF for Real-Estate Virtual Tours
You receive a curated dataset of 3 apartments, each with around 120 input images and known camera poses (already SfM-processed). Train a NeRF variant (Instant-NGP or Nerfacto re…
- Neural Scene Representation
- Nerf
- Pytorch
Open coursework - ResearchSeniorNew
Plan a Parameter-Efficient Fine-Tuning Strategy for a Big-Tech AI Lab
You will produce (1) a 6-page survey of four PEFT methods (LoRA, adapters, prefix tuning, IA3) with their strengths, weaknesses, and parameter footprints, (2) a one-page decisio…
- Parameter Efficient Fine Tuning
- Transfer Learning
- Fine Tuning
Open coursework - CodeSeniorNew
Auto-Tune a Distributed Training Cluster's Throughput
Pick a representative fine-tune job (an open 7B model on a public instruction dataset is fine). Define the search space: NCCL_ALGO, NCCL_PROTO, num_workers, prefetch_factor, gra…
- Distributed Training
- Hyperparameter Tuning
- Nccl
Machine Learning Systems - ResearchSeniorNew
Long-Context QA Evaluation Benchmark for Legal Memoranda
You receive 25 anonymized legal memoranda (50-90 pages each) and 100 QA pairs whose answers are deliberately spread across the documents (25 in pages 1-20, 25 in pages 20-40, 25…
- Long Context Qa
- Benchmark Design
- Model Evaluation
Open coursework - AnalysisBeginnerNew
Run an A/B Test on Two System Prompts for a Sales Email Assistant
You will (1) design the A/B test (random assignment by rep_id, 50/50 split, 2-week duration), (2) instrument three primary metrics: reply rate (event-based), average tokens per …
- Prompt Evaluation
- Ab Testing
- Metric Design
Open coursework - ResearchIntermediateNew
Sim-to-Real Domain Randomization for a Mobile Robot
You receive an Isaac Sim navigation environment, a baseline trained policy, a 50-episode real-bench test set (recorded sensor streams + ground truth) for offline policy evaluati…
- Domain Randomization
- Sim To Real
- Robot Navigation
Open coursework - ResearchSeniorNew
Model-Based RL for a Robotic Arm Pick-Place Task
You receive a PyBullet pick-and-place environment (Franka Panda arm, 12 object types, randomized starting poses) and a SAC baseline that hits 85% success after about 1.5 million…
- Model Based Rl
- World Models
- Reinforcement Learning
Open coursework - 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…
- Experiment Design
- Statistical Evaluation
- Human Evaluation
AI Measurement and Evaluation
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.
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