PyTorch Or TensorFlow
If you like applying PyTorch Or TensorFlow, every challenge here gives you a chance to practice it on a real industry brief.
- ResearchExpertNew
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
- Experimental Design
Robot Perception and Autonomy - DesignExpertNew
Design a Distributed Training Job for a 13B-Parameter Model
Decide whether to use Fully Sharded Data Parallel (FSDP), Tensor Parallelism, Pipeline Parallelism, or a hybrid; justify against the 13B-param + 32-H100 setup. Calculate memory …
- Distributed Training
- Fsdp
- PyTorch Or TensorFlow
Machine Learning Systems - ResearchExpertNew
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
- Experimental Design
Machine Learning - ResearchExpertNew
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 Or TensorFlow
AI Safety and Alignment Practice your coursework on real scenarios.
Every challenge is shaped from real industry context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- ResearchExpertNew
Price American Options with a Deep Hedging Notebook
Simulate price paths for a single underlying (geometric Brownian motion is fine as a baseline; bonus for stochastic volatility). Implement Longstaff-Schwartz Monte Carlo as the …
- Deep Learning
- Stochastic Modeling
- Derivatives Pricing
AI and Quantitative Finance - ResearchExpertNew
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 - CodeExpertNew
Profile and Cut Inference Cost on a Recommender at Scale
You receive (1) a frozen ONNX export of the production model, (2) a sample request trace of 24 hours at 1% sampling, and (3) a single A100-class GPU sandbox. Profile with NVIDIA…
- Gpu Profiling
- Model Quantization
- Inference Optimization
Machine Learning Systems - ResearchExpertNew
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.
- ResearchExpertNew
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…
- Supervised Learning
- Vision Transformers
- PyTorch Or TensorFlow
Advanced Deep Learning - ResearchExpertNew
Certify Robustness for a Medical-Imaging Classifier
You receive the classifier (a PyTorch ResNet variant) and a 4,000-image labeled validation slice. Apply randomized smoothing (Cohen et al.) at sigma in {0.25, 0.5, 1.0}. Report …
- Certified Robustness
- Randomized Smoothing
- Formal Verification
Trustworthy AI, Robustness, and Safety - ResearchExpertNew
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 - CodeExpertNew
Run a Backpropagation Bug-Hunt on an Open-Source RL Implementation
You receive the offending fork (around 4,000 lines of PyTorch) and three known-failure seeds. Reproduce the NaN failure deterministically, instrument the forward and backward pa…
- Backpropagation
- PyTorch Or TensorFlow
- Debugging
Deep Learning 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
- ResearchExpertNew
Audit a Production Model for Membership Inference Attacks
Use a black-box membership inference attack (e.g., the LiRA or shadow-model attack). You have query access to a sandboxed copy of the model + the original training data labels f…
- Membership Inference
- Privacy Attacks
- Model Evaluation
Privacy-Preserving Machine Learning - ResearchExpertNew
Investigate Scaling Trends on a Small Open Benchmark
You will train 4 transformer language models (10M, 50M, 200M, 600M parameters) on a public pretraining corpus (e.g., a small subset of FineWeb or OpenWebText) under identical op…
- Scaling Laws
- Transformer Pretraining
- Compute Optimal Training
Large Language Models - CodeExpertNew
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 - CodeExpertNew
Fuse Camera + Audio Cues for an Autonomous-Vehicle Edge Case
You receive a curated dataset of 4,000 short clips (5s each), each with synchronized 8-camera 360-degree video, 4-channel audio, and labels (siren-active emergency vehicle prese…
- Multimodal Perception
- Neural Networks
- Audio Processing
Machine Perception - ResearchExpertNew
Concept-Activation Vectors for an Autonomous-Vehicle Perception Audit
You receive a trained semantic-segmentation model (8 classes including pedestrian, vehicle, road, sky), an internal validation set of 2,500 driving frames, and a small concept-i…
- Tcav
- Concept Explanations
- Interpretability
Explainable and Interpretable AI - ResearchExpertNew
Embodied Visual Reasoning for a Warehouse Pick Assistant
Use an embodied simulator (Habitat 3.0 or Isaac Sim — pick one and justify) to render 300 cluttered-bin scenarios with a target item label. For each scenario, build two reasonin…
- Embodied Vision
- Vision Language Models
- Visual Reasoning
Visual Intelligence and Visual Reasoning - CodeExpertNew
Train a 3D Object Detector for Highway Trucking
Use the nuScenes or Waymo Open Dataset (open access) as your training and evaluation source. Fine-tune a strong baseline (e.g., CenterPoint or BEVFusion) and define an evaluatio…
- Object Detection
- Perception
- PyTorch Or TensorFlow
AI for Autonomous Vehicles - ResearchExpertNew
Curriculum RL for a Simulated Drone Inspection Task
You receive a PyBullet-based wind-turbine inspection simulator with parameterizable wind, blade orientation, and sensor noise. Design a 3-stage curriculum: (1) hover near a stat…
- Ppo
- Curriculum Learning
- Deep Rl
Reinforcement Learning - ResearchExpertNew
Investigate Why Our Generative Model Memorizes Training Data
Pick a small open-source diffusion model (e.g., a Stable-Diffusion-class community model trained on LAION-subset). Reproduce a published membership-inference + extraction probe …
- Generative Models
- Memorization Analysis
- Differential Privacy
Advanced Deep Learning - ResearchExpertNew
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 - CodeExpertNew
Train a GAN for Synthetic Defect Augmentation on a Factory Line
You receive a labeled defect dataset (12 defect types, ranging from 8 to 4,200 examples each), the production classifier, and a starter StyleGAN2-ADA codebase. Train a GAN per r…
- Gans
- Stylegan
- Data Augmentation
Generative AI - ResearchExpertNew
Validate a Foundation Model for Protein-Ligand Docking Acceleration
Pick 20 publicly available protein-ligand complexes from the PDBbind dataset (or similar public source). Use a published open-source structural foundation model (e.g., a Boltz-s…
- Foundation Model Evaluation
- Structural Biology
- Model Validation
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.
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