AI & Data
Statistics & Data Science Methods Challenges
Statistics & Data Science Methods challenges put you inside the work of drawing trustworthy conclusions from data. You'll build Statistics Fundamentals and Statistical Analysis, run Exploratory Data Analysis, Hypothesis Testing, Confidence Intervals, and Linear Regression, and design clean Sampling Methods.
From there you'll handle the harder edges — Bayesian methods, Causal inference, A/B testing with statistical significance, Monte Carlo Simulation, and Uncertainty Quantification — applying Experimental design the way data scientists actually do. Each challenge you solve earns a verified credential you can share with recruiters.
Recommended Challenges
· Experimental design Clear- All
- Data Analysis
- Experimental design
- Simulation
- Exploratory Data Analysis
- Statistical Analysis
- Uncertainty Quantification
- Logistic regression
- Cost Modeling
- Hypothesis Testing
- Monte Carlo Simulation
- A/B testing with statistical significance
- Linear Regression
- Time series basics
- Bayesian methods
- Causal inference
- Sampling Methods
- StrategyBeginnerNew
Pitch a Regulatory Sandbox Application for an Edtech AI Product
Read the EU AI Regulation's regulatory-sandbox provisions. Pick a member-state sandbox program (Spain, Norway-as-EEA, or a German-state pilot are publicly documented options) an…
- Regulatory Analysis
- Ai Governance
- Product Strategy
AI Law, Policy, and Regulation - 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
Stress-Test Scalable Oversight on a Tool-Using Agent
Design a sandwich-oversight study: pick a task domain where non-expert oversight is plausible but not trivial (e.g., reviewing data-analysis steps, checking small bug fixes, eva…
- Scalable Oversight
- Alignment Research
- Experiment Design
AI Safety and Alignment - ResearchSeniorNew
Experimental Design for a Fintech App's Savings Nudge
You are a behavioral data scientist at SaveSmart. Design a randomized controlled trial (RCT) to test the effect of a 'future self' nudge on savings behavior. Define treatment an…
- Experimental Design
- Statistical Analysis
- Nudge Theory
Behavioral Economics 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
- ResearchSeniorNew
Benchmark Reward-from-Feedback Methods on a Tabletop Pick-Place
You will use a Franka Panda arm in PyBullet on a 4-object pick-and-place task. For each of the three feedback methods, train a reward model and a downstream policy until converg…
- Reinforcement Learning
- Reward Learning
- Preference Comparison
Human-Robot Interaction - 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
3D Vision and Multi-View Geometry - ResearchIntermediateNew
Lab Project: Compare Three Architectures on Your Own Mini-Benchmark
Scope the problem yourself (suggested examples: sentiment classification on a niche domain, tabular anomaly detection, time-series forecasting on a public dataset). Define the t…
- Experiment Design
- Statistical Testing
- Pytorch
AI/ML Practicum and Hands-on Lab - 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.
- ResearchSeniorNew
Pretrain a Small Vision Transformer with Self-Supervised Learning
You receive 80,000 unlabeled 224x224 histology tiles plus 4,000 labeled tiles split into train/val/test. Pretrain a ViT-Small using a self-supervised method of your choice (DINO…
- Self Supervised Learning
- Vision Transformers
- Pytorch
Advanced Deep Learning - 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
Deep Reinforcement Learning - ResearchIntermediateNew
Run a Perceptual Study on Color Scales for a Climate Risk Map
Design a remote study (Prolific or similar, 60 participants screened for normal color vision via Ishihara plates online) with 3 task types: (1) value estimation, (2) anomaly det…
- Perceptual Study
- Color Scales
- Experimental Design
Information and Data Visualization - ResearchSeniorNew
Graph Transformer Research Probe for a Drug-Target Predictor
You receive a public drug-target interaction dataset (around 50,000 drug-target pairs with labels and molecular graphs), a strong GIN baseline, and a starter GraphGPS implementa…
- Graph Transformers
- Graph Neural Networks
- Message Passing
Machine Learning on Graphs 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
- 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
Robot Learning - 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 - 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
AI/ML Practicum and Hands-on Lab - 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
Machine Perception - 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
AI for Autonomous Vehicles - 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 - 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 - AnalysisSeniorNew
Cost-Quality Prompt Optimization at Scale
You receive 2,000 labeled code snippets (human rater consensus score 1-5) and budget for at most 8,000 API calls across the optimization run. Run a factorial sweep of 3 prompt s…
- Prompt Optimization
- Cost Quality Tradeoff
- Experiment Design
Prompt Engineering - 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
Machine Learning - 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
Question Answering and Conversational Systems - ResearchIntermediateNew
Reproduce a Vision-Model Paper Under a Reproducibility Standard
Pick a vision-model paper from CVPR or NeurIPS 2024-2025 with publicly available code and a manageable compute footprint (single-GPU under 24 hours). Reproduce the headline metr…
- Reproducibility
- Experimental Design
- Model Evaluation
AI Measurement and Evaluation - 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
Meta-Learning, Transfer Learning, and Multi-Task Learning
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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