Physics Experts Planning AI Project

Nutrition Experts — Planning AI Project About Vetto Vetto is a global platform that connects top-tier professionals to strategic Artificial Intelligence projects around the world. Our mission is to build trust, quality, and long‑term value within the AI ecosystem, for both exceptional talent and companies operating at the forefront of technology. About the project We’re recruiting nutrition experts to review and improve real‑world scenarios used to train AI planning assistants in a clinical and educational context. The AI model will act as a nutrition tutor — structuring dietary problems, mapping decision trees, and teaching evidence‑based reasoning to students and healthcare professionals. Your job is to think like a senior nutritionist: break down clinical or dietary challenges, identify alternatives, justify decisions with concrete data, and ensure the reasoning is both rigorous and clear enough to teach. Who can apply Professionals from any nutrition specialty — clinical, sports, hospital, eating behavior, pediatric, oncological, or community nutrition. Anyone with hands‑on experience diagnosing nutritional problems and making evidence‑based decisions. Final‑year undergraduate students in nutrition or dietetics with practical clinical experience are also welcome to apply. Specialties we’re looking for We are especially interested in professionals with expertise in one or more of the following areas: Clinical Nutrition (outpatient or inpatient) Sports Nutrition Hospital / Critical Care Nutrition Eating Behavior & Nutritional Psychology Pediatric Nutrition Oncological Nutrition Community / Public Health Nutrition Functional Nutrition Selection In this application, you will answer questions following the instructions below. If selected, you will be invited to review real nutrition case scenarios as part of the project. For the reasoning case, present a real nutritional problem you diagnosed or solved — a dietary assessment challenge, clinical intervention decision, micronutrient deficiency investigation, eating behavior issue, sports performance adjustment, etc. You may anonymize it. We are not evaluating whether your conclusion was right. We are evaluating how you think. This is just an illustrative example. Your application should include more detail, specific data points, and thorough reasoning for each discarded alternative. Part 1 — The Problem: describe the nutritional problem and what data or information you had available at the start (anthropometric data, lab results, dietary recall, clinical history, etc.). Part 2 — Your Journey: describe your reasoning in 3 steps. For each step, explain what you analyzed or decided and what specific metric, data point, or clinical finding drove that decision. Part 3 — Discarded Alternatives: for each of the 3 steps, list at least 2 hypotheses you considered but ruled out and explain what concrete data eliminated each one. “It wasn’t the case” is not a valid answer. Part 4 — Conclusion: describe the final nutritional recommendation or intervention and how the evidence you gathered led to it. Also highlight 1–2 key insights — the most important findings or turning points in your reasoning: a lab marker that confirmed your direction, a detail that ruled out a strong alternative, or a non‑obvious observation that most practitioners would have missed. Example: Part 1 — The Problem A 34‑year‑old female recreational runner came in reporting persistent fatigue and declining performance over the past 3 months. BMI was 21.4. She trained 5x/week and reported eating “healthy.” No obvious dietary restriction reported. Part 2 — Your Journey Step 1: Requested a full blood panel — ferritin came back at 8 ng/mL (well below the 20 ng/mL threshold for athletes), while hemoglobin was still within normal range at 12.1 g/dL. This distinguished iron deficiency without anemia from iron deficiency anemia, which changed the intervention approach. Step 2: Conducted a 3‑day dietary recall — despite adequate total iron intake (~14 mg/day), 90% of sources were non‑heme iron with high phytate consumption at the same meals (oats, legumes, seeds), significantly reducing absorption. Vitamin C intake at iron‑containing meals was near zero. Step 3: Assessed menstrual history and training load — she was in a high‑volume training block (peak mileage week) with regular menstrual cycles, confirming high iron demand without compensatory intake, rather than a pathological absorption issue. Part 3 — Discarded Alternatives Step 1 — Alternative 1: Overtraining syndrome / Ruled out by: ferritin level directly explained the fatigue; overtraining markers (mood, sleep, HRV) were normal. Step 1 — Alternative 2: Iron deficiency anemia / Ruled out by: hemoglobin at 12.1 g/dL was within normal range; intervention needed to be dietary, not medical. Step 2 — Alternative 1: Insufficient total iron intake / Ruled out by: dietary recall showed 14 mg/day, meeting general recommendations; the issue was bioavailability, not quantity. Step 2 — Alternative 2: Malabsorption disorder (e.g., celiac) / Ruled out by: no GI symptoms, no history, and the absorption pattern matched dietary inhibitors rather than a systemic issue. Step 3 — Alternative 1: Hormonal imbalance / Ruled out by: regular menstrual cycles and no other hormonal symptoms. Step 3 — Alternative 2: Supplement immediately with high‑dose iron / Ruled out by: dietary restructuring could solve the bioavailability gap without GI side effects from supplementation. Part 4 — Conclusion Root cause was low non‑heme iron bioavailability driven by dietary inhibitor pairing and high training demand. Intervention: redistribute iron‑rich meals away from high‑phytate foods, add vitamin C sources at each iron meal, and retest ferritin in 8 weeks. No supplementation was needed. Key insights: The critical distinction was ferritin vs. hemoglobin — normal hemoglobin would have led most practitioners to dismiss iron as the cause. The second turning point was the dietary recall pattern: adequate total intake masked by nearly zero bioavailability due to consistent inhibitor pairing. Compensation Payment will be US$ 60 (or R$ 300) per approved deliverable. Each task takes approximately 40–60 minutes. Refer People → Earn Money If you’d like to refer someone, you can earn $20 USD for each approved referral. To participate, go to the Opportunities page on the platform (https://work.vetto.ai/opportunities) and click “Refer & Earn” to share your personal referral link. AI is not allowed. If we spot AI use, we’ll block the application. This application form must be completed entirely in English or Portuguese.